Location: Location not imported yet.Title: DEVELOPING NITROGEN FERTILIZER RECOMMENDATIONS FOR CORN (ZEA MAYS L.) USING AN ACTIVE SENSOR) Author
Submitted to: Agronomy Journal
Publication Type: Peer reviewed journal
Publication Acceptance Date: 8/15/2008
Publication Date: 11/1/2008
Citation: Dellinger, A.E., Schmidt, J.P., Beegle, D.B. 2008. DEVELOPING NITROGEN FERTILIZER RECOMMENDATIONS FOR CORN (ZEA MAYS L.) USING AN ACTIVE SENSOR. Agronomy Journal. 100(6):1546-1552. Interpretive Summary: New technologies in remote sensing may dramatically change the way farmers develop nitrogen (N) recommendations for corn (Zea mays L.), potentially reducing some of the environmental shortcomings of current N recommendations. This study evaluates the success of using a ground-based sensor for making in-season N recommendations and applications to corn. Reflectance (590 and 880 nm) obtained with an active sensor when the corn was 40 cm tall was used to calculate a Green Normalized Difference Vegetation Index (GNDVI). The Economic Optimum N Rate (EONR) for N fertilizer applied at the same growth stage as when reflectance was obtained was strongly correlated (r2 = 0.84) to GNDVI. Traditional approaches to making N recommendations, including soil tests and chlorophyll meters, were less effective in estimating EONR than using the reflectance information obtained with this sensor. This sensor could be used to make N applications during the growing season based on the current field conditions of the corn crop, compensating for the inherent spatial and temporal variability of N availability.
Technical Abstract: Because of uncertainties associated with nitrogen (N) behavior in the environment, producers managing corn (Zea mays L.) production systems often over-apply N fertilizer to reduce the risk that N will limit corn yield. Remote sensing represents a potential opportunity to reduce these uncertainties with in-season assessments of crop N status. This study examines the relationship between economic optimum N rate (EONR) and reflectance from a ground-based sensor, and considers the potential of using this relationship to develop sidedress N recommendations for corn. Four different fields in central Pennsylvania with unique cropping histories were planted to corn during each of two years. Preplant whole plot treatments including a Control, 56 kg N per ha as ammonium nitrate (AN), and approximately 37-122 kg available N per ha as Manure were used to create a range of N conditions at each site. Split plot treatments included seven sidedress rates (0, 22, 45, 90, 135, 180, and 280 kg N per ha) and one preplant rate (280 kg N per ha) as AN in 9.1 x 4.5 m plots. The EONR was determined for each whole plot treatment at each site by evaluating the grain yield response to sidedress N treatments. An active sensor was used at the six-leaf growth stage (V6) to collect georeferenced canopy reflectance data in the 590 nm and 880 nm wavelengths, which were used to calculate the Green Normalized Difference Vegetation Index (GNDVI). Chlorophyll meter readings and Pre-Sidedress Soil Nitrate Test (PSNT) soil samples were collected at V6. The EONRs for the 24 preplant treatment / site combinations in this study ranged from 0 to 202 kg N per ha. Of the 10 non-N-responsive preplant treatment / site combinations, the PSNT correctly identified 9 of these and the chlorophyll meter correctly identified all 10. Generally, EONRs were smallest (0-55 kg N per ha) when the previous crop was a legume, while EONRs were greatest (93-202 kg N per ha) when the previous crop was corn. Nitrogen recommendations based on the PSNT and chlorophyll meter were generally better than preseason N recommendations. The EONR was strongly related to relative GNDVI (r2=0.84) for the Control and Manure preplant treatments; however, when AN was applied at planting, relative GNDVI was not as good an indicator of EONR (r2=0.20). Given the strong relationship between EONR and relative GNDVI observed in this study (except when AN was applied at planting), developing sidedress N recommendations for corn using an active sensor could be an effective N management tool in Pennsylvania.