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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #235423
Title: NITROGEN RECOMMENDATIONS FOR CORN: AN ON-THE-GO SENSOR COMPARED TO CURRENT RECOMMENDATION METHODS

Author
item Schmidt, John
item DELLINGER, ADAM - USDA-NRCS
item BEEGLE, DOUG - PENN STATE UNIV

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/5/2009
Publication Date: 7/15/2009
Citation: Schmidt, J.P., Dellinger, A.E., Beegle, D.B. 2009. Nitrogen Recommendations for Corn: An On-The-Go Sensor Compared with Current Recommendation Methods Agronomy Journal. 101(4):916-924.

Interpretive Summary: On-the-go sensors that measure crop reflectance represent a promising technology that one day might allow corn producers to spatially vary N fertilizer applications – a technology that could dramatically reduce the environmental impact of N fertilizers. Our objective was to evaluate the success of using crop reflectance compared to conventional methods of estimating the economic optimum N rate (EONR), thus a method for making an appropriate N recommendation for corn. Corn was grown under field conditions typical to the mid-Atlantic region, representing a wide range of potential EONRs, including: corn following corn, alfalfa, or soybean; notill or conventional tillage; some fields with a recent history of manure applications; and preplant treatments that included manure, N fertilizer, or zero N fertilizer. An index based on crop reflectance, described as the relative green normalized vegetation index (GNDVI), was as good an indicator of EONR as conventional methods such as: a general statewide preplant N recommendation, the presidedress nitrate test (PSNT), a chlorophyll meter, and the late season stalk nitrate test. Because relative GNDVI can be obtained simultaneously during an in-season N fertilizer application, the potential to accommodate within-field spatial and season-to-season temporal variability in N availability makes this an attractive approach to improving N use efficiency in corn.

Technical Abstract: Precision agriculture technologies provide the capability to spatially vary N fertilizer applied to corn (Zea mays L.) with the potential to improve poor N fertilizer use efficiency in a crop that traditionally receives large amounts of N fertilizer. The focus of this study was to evaluate the potential of improving N recommendations based on crop canopy reflectance compared to those recommendations derived by conventional approaches. Corn was grown at four field sites in each of two years. Preplant treatments included: zero fertilizer, 56 kg N per ha, and manure, in a randomized complete block design. Split plot treatments included the following N sidedress rates as ammonium nitrate: 0, 22, 45, 90, 135, 180, and 280 kg N per ha, and one at-planting N rate of 280 kg N per ha. Light energy reflectance values at 590 and 880 nm from the crop canopy (as measured from an active sensor), chlorophyll meter (SPAD) measurements, and the presidedress nitrate test (PSNT) results were obtained at sidedress (6th – 7th leave stage). The late season stalk nitrate (LSSN) test was determined at physiological maturity. The economic optimum N rate (EONR) was determined based on grain yield response to sidedress N rates using a quadratic-plateau response function. Relative green normalized difference vegetation index (GNDVI) and relative SPAD were based on proportional measurements from the zero sidedress treatment to the 280 kg N per ha at-planting treatment. The EONR from 24 preplant treatment – site-year combinations was related to: relative GNDVI (R2=0.76), the PSNT (R2=0.78), relative SPAD (R2=0.72), and the LSSN test (R2=0.64), suggesting that relative GNDVI was as good an indicator of EONR as any of these other more conventional tests. Because relative GNDVI can be obtained simultaneously with a sidedress N fertilizer application, the potential to accommodate within-field spatial and season-to-season temporal variability in N availability should improve N management decisions for corn production.