Refinement of the missouri corn nitrogen algorithm using canopy reflectance
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Refinement of the Missouri Corn Nitrogen Algorithm Using Canopy Reflectance. Newell Kitchen, Ken Sudduth, and Scott Drummond USDA-Agricultural Research Service Peter Scharf, Harlan Palm, and Kent Shannon University of Missouri. Active-light Reflectance Sensing.

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Refinement of the missouri corn nitrogen algorithm using canopy reflectance

Refinement of the Missouri Corn Nitrogen Algorithm Using Canopy Reflectance

Newell Kitchen, Ken Sudduth, and Scott Drummond

USDA-Agricultural Research Service

Peter Scharf, Harlan Palm, and Kent Shannon

University of Missouri


Active light reflectance sensing
Active-light Reflectance Sensing Canopy Reflectance

Objective: To assess on different Missouri soils the use of active crop-canopy reflectance sensors for assessing corn N need and developing algorithms for optimizing economic returns with variable-rate N fertilizer application.


Methods
Methods Canopy Reflectance

  • A total of 16 field-scale experiments were conducted over four growing seasons (2004-2007)

  • These fields represented three major soil areas of Missouri: river alluvium, deep loess, and claypan.

  • Multiple blocks of N randomized rate response plots were arranged end-to-end so that blocks traversed the length of each field. Each block consisted of 8 N treatments from 0 to 235 kg N/ha on 34 kg N/ha increments, top-dressed sometime between vegetative growth stages V7 and V11


  • For 2006 and 2007, a complete second set of field-length blocks were also established where 67 kg N/ha was uniformly applied over the set of blocks shortly after corn emergence.

  • Adjacent to and on both sides of the response blocks, N-rich (235 kg N/ha) reference strips were also established. These ran the full length of the field and were treated shortly after corn emergence.



  • Crop canopy reflectance sensor (Crop Circle model ACS-210, Holland Scientific, Inc., Lincoln, NE) measurements were obtained from the corn canopy of the N response blocks at the same time the Spra-Coupe was used to apply N rate treatments.

  • On the same day reflectance sensor measurements were also obtained from the N-rich reference strips.


  • Data analysis for these field studies included four major steps:

    1) Determining optimal N with quadratic-plateau modeling

    2) Processing of canopy reflectance senor data from response plots and the N-rich reference areas

    3) Relating modeled optimal N from step 1 with sensor measurements from step 2

    4) Developing optimized-profit algorithms relative to conventional producer N rates


Results
Results steps:


Optimal N Rate as a Function of steps:

Canopy Reflectance


Optimal N Rate as a Function of steps:

Canopy Reflectance


Developing optimized profit algorithms relative to conventional producer n rates
Developing Optimized-profit Algorithms Relative To Conventional Producer N Rates

Inputs:

  • Values and quadratic response curves from optimal N rate modeling

  • Field-measured SI values for each response block

  • A set price of corn grain and N fertilizer

  • A producer prescribed N rate for each site-year

    Variables that were optimized during the iterative phase included:

  • Slope and intercept values for the N recommendation, based on the equation: Nrec = a(1/SI) + b (SI = sufficiency index)

  • The minimum N rate to be applied by the algorithm

  • The maximum N rate to be applied by the algorithm

    The analysis was repeated on 12 subsets of data, based upon factorial combinations of the following two variables:

  • Three soil types & all soils combined

  • N applied at planting (0 kg N/ha, 67 kg N/ha, both combined)


Fertilizer To Grain Ratio (FGR) Using SI Units For Various Combinations Of N Fertilizer And Corn Grain Prices


General Shape of N Algorithm Combinations Of N Fertilizer And Corn Grain Prices

Maximum N

Increasing N

Minimum N


Optimal N Rate as a Function of Combinations Of N Fertilizer And Corn Grain Prices

Canopy Reflectance


Profit Potential Using the Canopy Sensors Combinations Of N Fertilizer And Corn Grain Prices

And Derived Algorithms Relative to

Fertilizer to Grain Ratio (FGR)


Nitrogen Saved Using the Canopy Sensors Combinations Of N Fertilizer And Corn Grain Prices

And Derived Algorithms Relative to

Fertilizer to Grain Ratio (FGR)


Subtle differences from the perspective of a canopy sensor
Subtle Differences from the Perspective Combinations Of N Fertilizer And Corn Grain Pricesof a Canopy Sensor


Active light source Combinations Of N Fertilizer And Corn Grain Prices

crop sensors

Chlorophyll meter


Summary of in field plant sensing for nitrogen management
Summary of In-Field Plant Sensing Combinations Of N Fertilizer And Corn Grain Pricesfor Nitrogen Management

  • A significant relationship between canopy sensor sufficiency index and optimal N rate was observed in about half of the field studies.

  • Combined across all sites meaningful algorithms were created to give assurance that these sensors could be used for variable-rate N applications.

  • Algorithms should be adjusted as fertilizer and grain prices vary.

  • The primary advantages of sensor-based measurements is improved accuracy to site-specific crop need.


Fields and situations most suited for sensor based variable rate nitrogen applications
Fields and Situations Most Suited Combinations Of N Fertilizer And Corn Grain Pricesfor Sensor-Based Variable Rate Nitrogen Applications

  • Fields with extreme variability in soil type

  • Fields experiencing a wet spring or early summer (loss of applied N) and where additional N fertilizer is needed (i.e., rescue N)

  • Fields that have received recent manure applications

  • Fields receiving uneven N fertilization because of application equipment failure

  • Fields coming out of pasture, hay, or CRP management

  • Fields of corn-after-corn, particularly when the field has previously been cropped in a different rotation

  • Fields following a droughty growing season


An Example of a N Rate Recommendation Algorithm Shown Relative to Canopy Sensor-Based Sufficiency Index (SI).


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