Development of On-the-go Nitrogen Application Algorithms for Cotton

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Development of On-the-go Nitrogen Application
Algorithms for Cotton

• Terry Griffin, Ph.D., CCA
• Assistant Professor Economics
• Dept. of Agricultural Economics Agribusiness

NEU Conference
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Motivation and Issue

• Analyses have been conducted for single datasets
• Failed to model multiple climates, systems, etc.
• Global response estimated from pooled dataset
• Derive on-the-go nitrogen application algorithms
• Objective analyze multi-state experiment data to
  estimate response between active sensor
  reflectance and cotton yield for on-the-go
  nitrogen management
• Develop on-the-go N application algorithm

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Data and Methods

• Pooled model
• Multiple site-years 13 states
• All data (sites and years) in single dataset
• Each sub-dataset controlled for heterogeneity
• Testing for heterogeneity between sub-datasets
• Correlation among
• yield, nitrogen rate, NDVI values and timing
• Regression analysis of pooled dataset

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Challenges

• Cotton in a perennial
• N rate sufficient to cause yield penalty
• Identifying appropriate timing for NDVI
• DAP, GDD60
• Testing functional forms of relationships
• Testing indexes for algorithm
• Variety, location, and GDD60 specific
• Global algorithm with local intercept/slope
  shifters

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Correlation Coefficients
  YLD NDVI Mid NDVI Late
YLD 1
NDVI Mid 0.21 1
NDVI2 Late -0.22 0.45 1
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Cotton Yield Response to Applied N

• Theoretically quadratic without plateau

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Cotton yield by N rate 7 site-years
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NDVI by N rate
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NDVI wrt N rate by Timing
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Summary and Future Work

• Analysis is on-going
• Have not determined optimum algorithm
• Use of GDD60 needs to be revisited

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Acknowledgements

• Funding from Cotton Inc.
• Collaborators in respective states
• John Kelley Graduate Student

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• Terry Griffin
• Assistant Professor - Economics
• 501.671.2182
• tgriffin_at_uaex.edu