Improving an Active‐Optical Reflectance Sensor Algorithm Using Soil and Weather Information
2018
Bean, G.M. | Kitchen, N.R. | Camberato, J.J. | Ferguson, R.B. | Fernandez, F.G. | Franzen, D.W. | Laboski, C.A.M. | Nafziger, E.D. | Sawyer, J.E. | Scharf, P.C. | Schepers, J. | Shanahan, J.S.
CORE IDEAS: Canopy sensor performance improved using site‐specific information. Evenness of early‐season rainfall is crucial for adjusting N recommendations. Adjusting N recommendations using measured vs. USDA mapped soil data performed alike Active‐optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea mays L.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in‐season (∼V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha⁻¹ for treatments receiving 0 and 45 kg N ha⁻¹ applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha⁻¹. Without adjustment, 20 and 29% of sites were within 34 kg N ha⁻¹ of EONR with 0 and 45 kg N ha⁻¹ at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha⁻¹ of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance.
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