Skip to content

Development of a protocol for farmer participatory validation of a corn nitrogen decision support system (DSS)

Development of a protocol for farmer participatory validation of a corn nitrogen decision support system (DSS)

Principal Investigator: Bill Deen

Research Institution: University of Guelph

Timeline: April 2017 – April 2019


  • Demonstrate on two medium textured soils in Ontario how nitrogen (N) and soil moisture supply interact across the corn lifecycle to influence yield potential.
  • Generate a peer reviewed manuscript that conclusively demonstrates that Maximum Economic Rate of Nitrogen (MERN) can be predicted from the delta-yield, which is the yield difference between zero N and a sufficient N rate, using a model based on biologically relevant parameters.
  • Develop a Corn N-Decision Support System (DSS) that considers moisture interactions with N on corn N demand and a protocol for farmer participation in validation.


  • The investigation of how moisture interactions with nitrogen (N) influence corn yield potential and corn N requirements in Ontario soils/cropping systems will help farmers better interpret the data they are collecting with precision agriculture technologies and improve their management of N inputs.
  • The development of an improved Corn N-DSS may lead to increased farm profits and minimize environmental N losses by improving the ability of farmers to predict optimal fertilizer N rates across their fields and across years.

Scientific Summary:

Corn is the largest single recipient of nitrogen (N) fertilizers applied to agricultural crops, yet less than half of the N fertilizer applied to corn is recovered in grain. Greater than 50% of fertilizer N remains at risk of exiting the agro-ecosystem before crop uptake. Low fertilizer nitrogen use efficiency (NUE) is an economic inefficiency with profound implications for global N cycling and N pollution. Optimal fertilizer N rates in corn varies across years and across fields, making it difficult to predict the correct rate. The primary reason for this variation in optimum N rates is because soil moisture affects both natural soil N supply and corn N demand. Existing N recommendation systems have had limited success in predicting this variation, because the effect of moisture availability on corn N demand is often ignored. The effect of moisture on corn N demand remains difficult to address, since this variation occurs after traditional side-dress timing, usually within the first 2 weeks of June.

The overall objective of this research was the development of an effective Corn N-Decision Support System (DSS). This research demonstrates that there exists three sources of variation that impact corn N requirements: supply, losses, and demand, and that precipitation affects not only supply and loss variation but also demand variation.  Current systems available to Ontario farmers do not adequately consider all three sources of variation. In fact, the existing General N Recommendations for corn are skewed towards estimation of supply variation and do not incorporate the effects of weather. The importance of late vegetative stage precipitation in determining corn N requirement appears to be increasing, in part due to the fact that at higher corn yield potential the N x water interaction effect on corn N demand becomes more important.

Given the growing importance of late vegetative stage precipitation on corn N demand in determining optimal N rate, late season N application strategies appear to be required to effectively optimize N in corn.  Results from this research confirm that late season N application strategies can be developed without compromising corn yield, although risks associated with volatilization or unavailability of late season N due to dry conditions need to be addressed.


Rainfall at later vegetative stages (V5-V12) can significantly influence corn fertilizer N requirements. Whereas in the past fertilizer N requirements were determined largely by N supply and N losses, increasingly N demand is driving corn fertilizer N requirement.  Existing decision support tools must be modified to include the effect of yearly variation of N demand (yield), which is in part driven by yearly variation in the amount of rainfall during the late vegetative stage.

Given the growing importance of late vegetative stage rainfall on corn N demand, split N applications are increasingly required to enable optimization of fertilizer N rates. Hence, split N application strategies must be developed where a portion of N is applied at planting that ensures sufficient N during the vegetative stage, with the remainder of required fertilizer N applied during later vegetative stages (around V13). It is possible for such a strategy to not compromise yield since a relatively low amount of N fertilizer applied at planting is needed to ensure sufficient N uptake for optimal vegetative growth and maintain yield potential. Then a supplemental optimal N rate adjusted for weather conditions is applied during later vegetative stages (around V13) that meets corn N needs to produce economically optimal yields.

Development/validation of a new/improved corn N DSS will require substantial data. An on-farm data generation approach is required to quickly obtain the amount of data needed to develop/validate a new N DSS. This research has identified key considerations for developing an on-farm protocol and has led to successful implementation of an on-farm “pilot” study in 2019 and 2020.

A new CORN N DSS is required that better captures all three sources of variation (supply, losses and demand) influencing corn N requirement, and that also captures the influence of weather, particularly precipitation. Substantial data are required to develop an improved DSS. Unfortunately, the existing corn nitrogen database is not suitable for this exercise since much of the data are at lower corn yield levels where the N x water interaction on demand may have been less of an influence. Also, corresponding weather and soil data are not available for much of the database. Realistically the best opportunity to generate the required data is through on-farm research.

This project, through research results and discussions with growers and industry, provides clear direction regarding how to set up these on-farm trials. Key concepts include: 1) N response measured by delta yield transect approach, 2) only need to identify “extreme” years and regions of field since high accuracy of N rate prediction is not required for farmer economic objectives, 3) a minimum data set that includes data to predict N x water interactions and is consistent with the concept of identifying extremes, 4) the protocol must be scalable, and 5) data must flow from farmer to researcher seamlessly.

As a result of this project, CFREF funding (approx. $450K) has been obtained by Dr. Bill Deen, Dr. John Sulik and Dr. Asim Biswas to implement a “pilot study” in 2019 and 2020. This study will conduct on-farm N response research trials on 5-7 farms/year. The overall goal/vision of this CFREF funded project is to validate the protocol for on-farm N response trials developed in the present study. The CFREF project will 1) demonstrate the ability to generate on-farm data necessary for developing/validating an improved corn N DSS that considers N supply, losses, and demand effects as well as interactions with weather, 2) develop a method for efficient flow of data from farmer to researcher, and 3) be scalable so that it can be implemented in 2021+ across numerous field sites. 

External Funding Partners:


Project Related Publications:

Banger, K., Nasielski, J., Janovicek, K., Sulik, J., & Deen, B. 2020. Potential Farm-Level Economic Impact of Incorporating Environmental Costs into Nitrogen Decision Making: A Case Study in Canadian Corn Production. Frontiers in Sustainable Food Systems: 4.

Janovicek, K., Banger, K., Sulik, J., Nasielski, J., & Deen, B. 2021. Delta yield–based optimal nitrogen rate estimates for corn are often economically sound. Agronomy Journal: 113(2), 1961–1973.

Nasielski, J., & Deen, B. 2019. Nitrogen applications made close to silking: Implications for yield formation in maize. Field Crops Research: 243, 107621.

Nasielski, J., Earl, H., & Deen, B. 2019. Luxury Vegetative Nitrogen Uptake in Maize Buffers Grain Yield Under Post-Silking Water and Nitrogen Stress: A Mechanistic Understanding. Frontiers in Plant Science: 10.

Nasielski, J., Earl, H., & Deen, B. 2020. Which plant traits are most strongly related to post-silking nitrogen uptake in maize under water and/or nitrogen stress? Journal of Plant Physiology: 244, 153059.

Nasielski, J., Grant, B., Smith, W., Niemeyer, C., Janovicek, K., & Deen, B. 2020. Effect of nitrogen source, placement and timing on the environmental performance of economically optimum nitrogen rates in maize. Field Crops Research: 246, 107686.

Niemeyer, C., Nasielski, J., Janovicek, K., Bruulsema, T., & Deen, B. 2021. Yield can explain interannual variation in optimum nitrogen rates in continuous corn. Nutrient Cycling in Agroecosystems: 121(1), 115–128.