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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #348957
Title: Improving an active-optical reflectance sensor algorithm using soil and weather information

Author
item BEAN, G - University Of Missouri
item Kitchen, Newell
item CAMBERATO, J - Purdue University
item FERGUSON, R - University Of Nebraska
item FERNANDEZ, F - University Of Minnesota
item FRANZEN, D - North Dakota State University
item LABOSKI, C.A.M. - University Of Wisconsin
item NAFZIGER, E - University Of Illinois
item SAWYER, J - Iowa State University
item SCHARF, P - University Of Missouri
item SCHEPERS, J - Retired Non ARS Employee
item SHANAHAN, J - Fortigen

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2018
Publication Date: 9/13/2018
Citation: Bean, G.M., Kitchen, N.R., Camberato, J.J., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C., Nafziger, E.D., Sawyer, J.E., Scharf, P.C., Schepers, J.S., Shanahan, J.F. 2018. Improving an active-optical reflectance sensor algorithm using soil and weather information. Agronomy Journal. 110(6):2541-2551. https://doi.org/10.2134/agronj2017.12.0733.
DOI: https://doi.org/10.2134/agronj2017.12.0733

Interpretive Summary: Crop canopy sensors detect light reflected from crop canopies that can be used as an indicator of the nitrogen health of plants. From these measurements a nitrogen fertilizer recommendation can be generated. However, studies have shown the calculations that are used for generating corn N fertilizer rate recommendations from canopy sensor measurements (typically called nitrogen fertilizer algorithms), can often be inaccurate when evaluated over a wide range of corn growing conditions. Thus research is needed to know how to improve algorithm calculations. The objective of this research was to determine if soil and weather information from the time of planting to the time of canopy sensing could be used to improve the calculation for nitrogen fertilizer recommendations. From 49 sites spread out over the US Corn Belt, calculated nitrogen fertilizer recommendations from canopy sensing were compared to the economic optimal nitrogen fertilizer rates (EONR). Without adjusting the calculation, canopy sensing on average over these 49 sites underestimated crop nitrogen need by 66 lbs nitrogen/acre. When the calculation was adjusted using weather (e.g., evenness of rainfall) and soil (e.g., clay content in the upper part of the soil profile) information, the N recommendation was improved, with the recommendation underestimating crop N need by only about 44 lb nitrogen/acre. Without adjustment, only about 25% of the sites were within 30 lbs nitrogen/acre of EONR. With soil and weather adjustment this increased to almost 50% of the sites. Thus weather from early in the growing season and measurements of soil properties can be used to improve canopy sensing nitrogen fertilizer recommendations. Farmers benefit from this research because they can more accurately determine how much nitrogen to apply to meet corn crop needs. This fertilizer assessment approach linked with variable-rate fertilizer application is especially helpful when managing fields that show extreme variation in crop N need. Such a strategy helps target nitrogen applications and will help reduce areas of excess nitrogen application within fields, which will save farmers money. Additionally, as fertilizer can be better matched with crop need, nitrogen loss to lakes and streams will also be reduced.

Technical Abstract: Active-optical reflectance sensors (AORS) use measured light reflectance characteristics from crop canopies as a surrogate bioassay of the plant’s N health and generate fertilizer N recommendations. However, studies have shown AORS algorithms used for corn (Zea mays L.) N rate recommendations are not consistently accurate. Thus, AORS algorithm improvements should be explored. The objective for this research was to determine if soil and weather information could be utilized along with an AORS algorithm developed at the University of Missouri to improve in-season (~V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across 8 US states in the corn belt region over 3 growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer recommendations from the algorithm were compared to the calculated economic optimal N rates (EONR). Without soil and weather information included, the RMSE of the difference between the algorithm and EONR was 81 and 74 kg N/ha for treatments receiving 0 and 45 kg N/ha applied at planting, respectively. When the algorithm was adjusted using weather (seasonal precipitation and distribution) and soil clay content, the RMSE of the difference was reduced by 25 kg N/ha. Without adjustment, 20 and 29% of sites were within 34 kg N/ha of EONR with 0 and 45 kg/N ha at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N/ha of EONR. Thus, using site-specific weather and soil information could be used to improve the performance of AORS N fertilizer recommendations.