|BEAN, G - University Of Missouri|
|CAMBERATO, J - Purdue University|
|FERGUSON, R - University Of Nebraska|
|FERNANDEZ, F - University Of Minnesota|
|FRANZEN, D - North Dakota State University|
|LABOSKI, C.A.M. - University Of Wisconsin|
|NAFZIGER, E - University Of Illinois|
|SAWYER, J - Iowa State University|
|SCHARF, P - University Of Missouri|
|SCHEPERS, J - Retired Non ARS Employee|
|SHANAHAN, J - Fortigen|
Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/22/2018
Publication Date: 11/1/2018
Citation: Bean, G.M., Kitchen, N.R., Camberato, J.J., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C., Nafziger, E.J., Sawyer, J.E., Scharf, P.C., Schepers, J.S., Shanahan, J.F. 2018. Active-optical reflectance sensing corn algorithms evaluated over the United States Midwest corn belt. Agronomy Journal. 110(6):2552-2565. https://doi.org/10.2134/agronj2018.03.0217.
Interpretive Summary: Knowing the right amount of nitrogen fertilizer to apply on corn is challenging due to year-to-year changes in crop nitrogen need and variation in the amount of inorganic nitrogen present in soil and how that changes over the growing season. Crop canopy reflectance sensing has proven effective in some fields for assessing corn nitrogen health. Additionally these reflectance measurements have been used to calculate nitrogen fertilizer recommendations, often called nitrogen fertilizer algorithms. However, the different algorithms that have been developed for converting reflectance information into nitrogen fertilizer recommendations have not been tested across a broad geographical region. The objective of this research was to evaluate across the US Midwest Corn Belt the performance of three canopy reflectance algorithms used for making corn nitrogen fertilizer recommendations. Nitrogen recommendation rates estimated from algorithms were compared to the end-of-season calculated economic optimal nitrogen fertilizer rate (EONR). No algorithm was within 30 lbs of nitrogen/acre of EONR more than 50% of the time. When 40 lb of nitrogen/acre was applied at planting, on average the algorithms under-recommended how much N to apply by 66 to 105 lb of nitrogen/acre. The algorithms performed slightly worse when no nitrogen was applied at planting. With two of the algorithms, utilizing the red edge reflectance waveband instead of the red waveband improved nitrogen recommendations slightly. These findings demonstrate that while these canopy reflectance algorithms may have worked well within the state from which they were created, performance across the whole Corn Belt region could be described as “poor to fair”, depending on which algorithm was used. Adoption of canopy sensing technology by farmers is contingent on algorithms that can consistently recommend an accurate nitrogen fertilizer recommendation. As such, this work demonstrates the need for further algorithm development in order to better capture diverse soil and weather conditions. Proven algorithms will improve profits for farmers and benefit the general public by reducing field losses of nitrogen into streams.
Technical Abstract: Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Active-optical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate across the US Midwest Corn Belt the performance of AORS algorithms used for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and topdress N rates (45 to 270 kg N/ha on 45 kg/ha increments) applied at approximately the V9 corn development stage. Nitrogen recommendation rates estimated from AORS algorithms were compared to the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N/ha of EONR >50% of the time. Average recommendations differed from EONR 81 to 147 kg N/ha with no N applied at planting and 74 to 118 kg N/ha with 45 kg of N/ha at planting, indicating algorithms performed slightly worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. These findings demonstrate that while these AORS algorithms may have worked well in the local region from which they were created, further algorithm development is needed when utilizing this technology over geographically-diverse soil and weather conditions.