Active-Crop Sensor Calibration Using the Virtual-Reference Concept K. H. Holland (Holland Scientific) J. S. Schepers (USDA-ARS, retired) 8 th ECPA Conference 2011

“N-Rich” Reference Postage-stamp calibration Ramped calibration strip Randomized calibration block (field strips) check N-rich other N rates

Modified Postage Stamp Combine-width plots Randomized except check plot Each block of treatments should have minimal soil variability Repeated replications check

Modified Calibration Ramp Standard ramp of plots No randomization Each N rate in the ramp has a nearby check and adequate N reference Could randomize N rates in the ramp strip check N-Rich

Commercial applicators and large producers - - TELL US : N-rich strips are problematic – May be hard to locate (legal problems) – Need to move each year Can not expect operators to understand how the algorithm and sensor calibration work Need a “ turn-key ” approach that does not require N-rich strip or highly-skilled operator Algorithm needs to be simple, versatile, and easy to adapt for local conditions

Algorithms 1)based on predicted yield potential (Oklahoma State University) (GreenSeeker) 2) based on producer-set minimum and maximum N rates (Europe & Missouri) 3) based on extension of crop N-response function (Holland and Schepers) Note: All algorithms use sensor data that are normalized to “healthy crops”.

In-Season N Management Crop vigor during the growing season is proportional to yield at harvest

How to Characterize Healthy Crops ? N-Rich Strip (or Ramp Calibration Strip) average (as with plot studies) programmed (highest 3 consecutive seconds) Normal Field Transects identify healthy plants from frequency distribution of all plants (histogram) (MS Excel)

Mexico - White Corn, 2010 Crop Circle 600 ~6 kmph

Mexico - White Corn, ~6 kmph Crop Circle 95 Percentile 3-second Running Average = Percentile = % lower

Field Average

N Credits Preplant N EONR Producer Optimum N Accumulation (based on growth stage) Sufficiency Index Back-Off Strategy SI to start cutback SI to cut-off Algorithm Spatial Soil / Topography Adjustment Field Reference

Holland K.H. and J.S. Schepers Derivation of a variable rate nitrogen application model for in-season fertilization of corn. Agronomy Journal 102: S e e N appl = ( N opt – N cred ) √ √ (1 – SI) ∆ SI Farmer Rate or N EONR

Uniform Rate

N Rates (0, 50, 100, 150, 200 kg/ha)

OptRx Check Plot Soybean Previous Year

95 Percentile

CI red-edge values : 95 percentile second average % lower GreenSeeker

Irrigated Corn V9 Growth Stage 95 percentile

Virtual Reference Strip (0-200 kg N/ha preplant) check

Mexico, 2010 Drive and Apply

There’s Probably a Lot More Information in a Histogram than We Realize ! Where’s it at ? How to get it out ?

Mexico Irrigated Corn V5 Growth Stage SI = 0.7

Mexico - White Corn, ~6 kmph Crop Circle 95% Cut-back level

Conclusions The virtual reference concept offers producers a convenient approach to quantify the vigor and chlorophyll status of crops for in-season N applications. Histograms of active sensor data and related analyses offer a quick glimpse of where to focus management efforts. New sensors and tools will be needed to help fine tune management decisions.

Jim Schepers

Historic Perspective N-Rich treatment was initially used to normalize data from plot studies and allow leaf N concentration comparisons across time, fields, cultivars, etc. (1988) Extended to normalization concept to SPAD meters. (1990) Adapted to field situations and N-Rich strips to accommodate crop canopy sensors. (~2000)

Historic Perspective N-Rich plot concept extended to postage stamp arrangement with multiple N rates. (2002) Ramped calibration strip with multiple N rates introduced. (2005) Need for active sensor calibration technique to accommodate commercial applications. (2007)