Advanced Agrilytics

NITRAPYRIN A Deeper Look Into Efficacy, Yield Response

Yield targets, in season sampling, or more recently, modeling efforts, have all made attempts at driving nitrogen rate decisions. The challenge, however, is oftentimes these lines of thinking do not take into consideration the variability of denitrification and leaching that occur across field spatial variability. We identified that while nitrification inhibitors are used to mitigate N loss under saturated soil conditions, research had not yet described variance in the effective use rate of these products across sub-field environments. So, we did. We established our Nitrogen Loss Potential (NLP) data layer to inform decisions in the sub-field environments and allow us to conduct research trials specifically isolating nitrogen management. This capability led to a spatial efficacy evaluation for the nitrification inhibitor Nitrapyrin.


Nitrogen Loss Potential Layer Used to Place Field Treatments

Our Nitrogen Loss Potential data layer allowed us to place treatments in pre-meditated locations to better understand and analyze the Nitrapyrin research. We identified spatial dependence for a Nitrapyrin yield benefit, as well as the response rate at a granular, sub-field environment.

The findings led to an exclusive variable rate Nitrapyrin prescription that is available to Advanced Agrilytics customers, allowing us to increase their probability of success, while adding consistency to the response across multiple years.


Rate Response by Sub-Field Environment

To understand the impact different field attributes have on N availability, a mechanistic modeling approach of the N cycle was undertaken to develop a proprietary Nitrogen Loss Potential (NLP) data layer. By being able to characterize the environment with respect to NLP across a field, we are able to conduct research trials specifically isolating nitrogen management.

A field scale project was conducted in 2015 and 2016 to evaluate the spatial efficacy of the nitrification inhibitor Nitrapyrin. Utilizing the NLP layer to place treatments, as well as analyze the data, we were able to identify a spatial dependence for a Nitrapyrin yield benefit and a rate response by sub-field environment. These findings led to the creation of a proprietary variable rate Nitrapyrin prescription that is utilized for our Advanced Agrilytics (AA) customers, leading to an increased probability of success and response consistency across multi-year use.

We believe that a similar approach can be undertaken with other nitrification inhibitors to identify the spatial factors that influence both product efficacy and application rate. By documenting this information, we can improve product positioning in terms of an applications’ field specific efficacy and guide timing of nitrogen and stabilizer applications.