Sensor-Based Approaches for Cotton Nitrogen Management

Outline

Introduction 2008 Sensor Projects Sensor-based approaches to manage nitrogen Core data collection

Cotton Belt

USDA, NASS

Cotton and Nitrogen

Perennial plant managed as annual Indeterminate flowering pattern

50 lbs N – per lint bale (1 bale = 480 lbs) Over-application of N:

Energy partition to vegetative vs. reproductive development

Large plants prevent efficient harvest Growth regulators applied to control vegetative

development

2008 Sensor ProjectsState Investigators Inputs Managed Based on Modulated Active Light Sensors

PGR Defoliant Nitrogen Insecticide Plant Map Irrigation

Alabama S. Norwood, A. Winstead, J. Fulton

K. Balkcom

Arizona T. Clarke, D. Hunsaker

Arkansas M. Mozaffarri

Georgia G. Vellidis, C. Perry

H. Schomberg

Louisiana R. Leonard

B. Tubana, D. Boquet, E. Clawson

Mississippi J. Varco

Missouri G. Stevens, P. Scharf, E. Vories

New Mexico T. Carrillo, J. Ellington

North Carolina G. Robinson

Oklahoma R. Taylor, T. Sharp, S. Osborne, J. Banks, B. Arnall, B. Raun

South Carolina M. Jones, P. Bauer

A. Khalilian, W. Henderson

Tennessee J. Wilkerson

O. Gwathmey

J. Larson (Economics)

Texas K. Bronson

D. Martin

A. Thomasson, R. Sui customsensor

+ other sensor

+ other sensor

+ other sensor

+ other sensor

Sensor-Based Approaches

Relate yield potential with sensor readings and a well fertilized nitrogen plot

Direct regression relationship between sensor reading compared to a reference nitrogen plot

A growth stage specific relationship between the sensor reading and N rate

Relationship between historic yield, soil type and the sensor reading

Relate yield potential with sensor readings and a well fertilized N plot

Nitrogen RecommendationVariables that determine crop N requirements

Estimation Procedures

Crop yield level Yield goal; history(Yield potential predictive equation)

Available soil N organic matter mineralization residual N atmospheric deposition legumes credit nitrate in irrigation water manure

Soil and tissue analyses

(Estimates of increase in yield due to N using early-season canopy reflectance- response index)

SBNRC AlgorithmOSU – Arnall et al.

• N Rate = (YP0 * RI – YP0) * %N / NUE

• potential cotton lint yield, kg/ha = 235.96 e 2216.2 * INSEY

• cotton lint yield, kg/ha = 177.41 e 2216.2 * INSEY

• Where:• Yield Prediction Model: YP0 = 235.96 e 2216.2 * INSEY

• Response Index: RI = 1.8579 * RINDVI – 0.932• %N = 0.09• Nitrogen Use Efficiency: NUE = 0.50

YP0 = 235.96e2216.2x

y = 177.41e2216.2x

R2 = 0.6905

0

400

800

1200

1600

0.000 0.000 0.001 0.001 0.001

Cum INSEY (NDVI / Cumulative GDD's)

Lint

Yie

ld (k

g ha

-1)

LCB06-886CumGDDLCB06-949CumGDDLCB06-1161CumGDDLCB06-1215CumGDDLCB07-1015CumGDDLCB07-1152CumGDDLCB07-1262CumGDDSWR-846CumGDDSWR-999CumGDD

SBNRC AlgorithmOSU – Arnall et al.

LSU AgCenter Implementation of OSU approach (Tubana et al.)

Initiated in 2008, 3 sites in North Louisiana Growth stage: two weeks after early bloom INSEY is defined as NDVI divided by the number of days from planting to sensing

y = 6.4676e430.14x

r2 = 0.7377

0

200

400

600

800

1000

1200

1400

0.006 0.007 0.008 0.009 0.010 0.011 0.012 0.013

INSEY (NDVI/number of days from planting to sensing)

Lint

yie

ld, l

bs/a

cre

NERS-slNERS-clMRRS-sl

y = 5.3155x - 4.2768r2 = 0.4038

0

0.5

1

1.5

2

2.5

3

0.8 0.9 1 1.1 1.2Response Index (NDVI)

Resp

onse

Inde

x (H

arve

st)

NERS-slNERS-clMRRS-sl

LSU AgCenter Implementation of OSU approach (Tubana et al.)

This study also consider the impact of PGR (applied at early bloom) on the yield relationship.

y = 5.2758e444.66x

r2 = 0.7464

y = 6.5961e432.91x

r2 = 0.7634

0

200

400

600

800

1000

1200

1400

0.0070 0.0080 0.0090 0.0100 0.0110 0.0120 0.0130

INSEY (NDVI/number of days from planting to sensing)

Lint

yie

ld, l

bs/a

cre

Without PGR With PGR

LSU AgCenter Implementation of OSU approach (Tubana et al.)

Clemson Modification of OSU Approach (Khalilian et al.)

Soil specific yield by INSEY relationships (soil electrical conductivity categories)

Coastal plain soils of South Carolina INSEY was calculated as days from emergence to date of sensing.

HighEC: Medium Low

R2 = 0.9818

R2 = 0.9694

R2 = 0.9798

1700

2000

2300

2600

2900

3200

3500

0.008 0.01 0.012 0.014 0.016

INSEY

Seed

Cott

on Y

ield

INSEYSe

ed C

otton

Yie

ld y = 413.46e 104.98x

R2 = 0.9103

0

1000

2000

3000

4000

0 0.005 0.01 0.015 0.02 0.025

Direct regression relationship between sensor reading compared to a reference nitrogen plot

Missouri (Scharf et al.)

Best-band-ratio predictor of economically optimal nitrogen rate

y = 128.12x - 94.249R2 = 0.59

0

50

100

150

200

250

0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75

Greenseeker Red/NIR Relative to High N Plots

Opt

imal

N R

ate,

lb N

acr

e-1

Missouri (Scharf et al.)

Calculating N rate based on NDVI and refNDVI: Crop Circle 210: N rate (lb/ac) = 573 - [549 x (NDVI/refNDVI)] GreenSeeker: N rate (lb/ac) = 760 - [732 x (NDVI/refNDVI)] Cropscan: N rate (lb/ac) = 691 - [673 x (NDVI/refNDVI)]

Ceilings on total N rate are use: 200 lbs N/ac for heavy soils (clay, clay loam) 150 for other soils

Farmers decide on lowest and highest N rates for the system (within the ceiling)

A growth stage specific relationship between sensor readings and N rate

Mississippi (Varco et al.)

Relationship between GNDVI or NDVI at a specific stage of growth and fertilizer N rate

Leaf N approach (graph): Utilize N rates to that produce leaf N values at various physiological stages

Mississippi (Varco et al.)

Example of established relationship at the third week of squaring (experimental data):

Fertilizer N rate equivalence (FNRE) = -999.562 + 1584.984(GNDVI or NDVI on-the-go value)

Variable N rate = FNRE (experimental data) – target rate Ceilings on total N rate (cutoff values)

Relationship between historic yield, soil type, and sensor readings

Tennessee (Wilkerson et al.)

Bracket the response of real-time nitrogen prescription algorithms Incorporate a map of historic information: identify areas within a field

as historically low or high yielding and increment/decrement the N prescription accordingly

Additional agronomic data to determine the size of increment or decrement in response to yield potential is needed.

Historic Yield Potential

NDVI Low Average HighHigh R-- R- RAverage R- R R+Low R R+ R++Where: R is the average recommended rate, R+ and R++ are positively incremented, and R- and R-- are decremented from the recommended rate.

Texas (Sui et al.)

Canopy chlorophyll content index (CCCI) – relative estimate of the chlorophyll content in a crop canopy

CCCI accounts for variation in canopy density and less likely to give false indications of low chlorophyll content (vs. simple ratio or NDVI)

Well-suited for the three-band sensor (Crop Circle)

Core Data Collection

Develop a simplified yield potential nitrogen rate estimate – under discussion.

Producer usable approach - 2010

Core Data Collection Growth stages for plant data collection:

Early squaring: 3 to 7 squares Early bloom: 2 to 3 blooms Mid bloom Peak bloom

Nitrogen rates: 0, 90, and 120 lb N/ac

Core Data Collection Soils data set (Mehlich-3 P & K, soil NO3)

Preplant samples Post-harvest samples

Core Data Collection Within season plant information:

Sensor readings

Core Data Collection Within season plant information:

SPAD readings and plant height Tissue samples for leaf N

Hand harvest – 13.3 ft of row Machine – 40 feet of 2 rows Determine seed cotton and lint weight Seed N content

Yield Data Collection

Other Record Keeping

Varieties Planting and harvest dates Nitrogen rates, timing, application methods and N

sources PIX rates and dates (if applicable) Irrigation amounts (indicate in furrow, pivot, drip) Rainfall

Take Home Notes

One or more of these approaches should be ready for extended on-farm evaluation in 2010.

Will this end with one concept: analysis of core data: correction procedures (thermal time, days from planting

to sensing etc.) components (yield potential, response index, simple ratio)

Questions!!!