khosla presentation info ag 2017 handout...8/1/2017 prof. r. khosla, colorado state university 2...

8/1/2017

Prof. R. Khosla, Colorado State University 1

Nitrogen Management

Raj KhoslaColorado State University

https://www.euractiv.com/wp‐content/uploads/sites/2/2016/10/Digital‐farming.jpg

Nitrogen management

?I

Adapted from: Lassaletta et al. 2014 Environmental Research Letters

Nitrogen Use Efficiency

(<50%)

8/1/2017


Ammoniumfertilizer

NH3

NH4+

NH3

N2

NO2- NO3

-

N2O,NO, NO2

OM

Urea

N2O

RunoffLeaching

NH3

Forms Of Nitrogen

Only two are plant available: NO3- and NH4

+

NO2‐

NO2

HNO2

HNO3

NO‐

N2

N2O

NH2OH

N2H4

NH3NO3

‐

NH4+

How do we manage nitrogen for crop production?

CSUAgricultural Research,

Development & Education Center

Eastern Colorado Research Center

Arkansas Valley Research Center

San Luis Valley Research Center

Plainsman Research Center

Southwestern Research Center

Western Research Center

200 lbs

150 lbs0 lbs

50 lbs

200 lbs150 lbs

0 lbs50 lbs

8/1/2017


Calculating the Optimal N Rate

Nitrogen Rate (lbs/Acre)

Gra

in Y

ield

(B

u/A

cre)

Optimal Range

N rate = 35+ (1.2 X EY (bu/ac))

N ManagementState N Rate Recommendation

CO 35+ (1.2 X EY (bu/ac)) – (8 X Average ppm NO3 N in Soil) – (.14 X EY (bu/ac) X %OM)‐ Other N

Credits

KS (1.6X YG (bu/ac))‐(%OM X 20) ‐ Profile N ‐ Legume N‐ other N Credit

OH ‐27 + (1.36 X Yield Potential (bu/ac) ‐100) – N credit (lb/ac) or 110 + [1.36 X (Yield potential

(bu/ac) ‐100)] – N credit (lb/ac)

IN ‐27 + (1.36 X Yield Potential (bu/ac) ‐100) – N credit (lb/ac) or 110 + [1.36 X (Yield potential


MI ‐27 + (1.36 X Yield Potential (bu/ac) ‐100) – N credit (lb/ac) or 110 + [1.36 X (Yield potential


MO Fertilizer N Recommendation (lbs/ac) – Pre‐plant N Test Credits (lbs/ac)

MT N Fertilizer YG Recommendation (lbs/ac) ‐ PSNT NO3‐ (lbs/ac) *Wheat

ND Fertilizer N recommendation (lbs/ac)‐ Soil Nitrate Concentration (lbs/ac)‐ N Credits (lbs/ac)

NE 35+ [1.2 X EY (bu/ac)] – (8 X Average ppm NO3 N in Soil) – (.14 X EY (bu/ac) X %OM)‐ other N

credits

OR YG (bu/ac) X Required N Protein Goal (lb/ac) – Residual Soil N (lb/ac) *Wheat

PA EY (bu/ac) – ( (Manure since last harvest (lb/ac) + Previous Crop Factor (lb/ac) + Three year

Manure History Factor (lb/ac)) X Soil Nitrate (lb/ac))

SD YG bu/ac X 1.2 – Soil NO3 (lbs/ac) – Manure N (lb/ac) + no‐till Adjustment

VA EY (bu/ac) – ((Applied Manure Factor Last Year (lb/ac) + Leguminous Crop Factor (lb/ac) +

Manure History Factor (lb/ac)) * (PSNT (ppm))

IA N Rate Web Application

WI N Rate Web Application

MN N Rate Web Application

IL N Rate Web Application

ND N Rate Web Application

Common VariablesState N Rate Recommendation

CO 35+ (1.2 X EY (bu/ac)) – (8 X Average ppm NO3 N in soil) – (.14 X EY (bu/ac) X %OM)‐ other N Credits

Estimated Yield (EY)

Soil N Test

N Credits

Web Application

Max Economic Return To Nitrogen

8/1/2017


+/- 2 bu/A from the mean

%+/- 10 bu/A from the mean

Only 36%

Mean: 182.5 bu/A

>192.5 bu/A

40%Under-fertilized

<172.5 bu/A

24%Over-fertilized

Yield MapPixels = Average?

8%

high

med

med

low

low

Management Zones are delineated on farm fields by classifying the field into different sections or zones.

* CSU, USDA-ARS, Centennial Ag Inc.

Based on the research conducted in Colorado*

N rate = 35+ (1.2 X EY (bu/ac))

Average

In 9 out of 10 site years we can separate low from high zone but NOT low from medium or medium from high zones based on grain yield

Mean grain yield across MZs

16

12

8

4

0

a a b

Low Medium High

Management zones

Gra

in y

ield

(M

g ha

-1)

12

9

6

3

0

ab b

Low Medium High

Management zones

Gra

in y

ield

(Mg

ha -1

)

a

20

15

10

5

0

b b

Low Medium High

Management zones

Gra

in y

ield

(M

g ha

-1)

a

Source: Koch et al. 2004

Low Productivity (Zone 3)

MediumProductivity (Zone 2)

High Productivity (Zone 1)

The three data layers

Aerial Imagery

Topography

Farmer’s experience

are stacked as GIS layersto delineate the zone

Traits such as dark color, low-lying topography, and historic high yields were designated as a zone of potentially high productivity or high zone

Delineating management zones…

8/1/2017


Macro-variability

Micro-variability

Low Productivity (Zone 3)

MediumProductivity (Zone 2)

High Productivity (Zone 1)

Landsat 8

Worldview‐2 Natural Color display (Bands 5‐3‐2)

Captured 9/13/2013Spatial Resolution: 2m

Boulder Creek Flood Plain

Landsat 8Natural Color display (Bands 4‐3‐2)

Captured 9/17/2013Spatial Resolution: 30mBoulder Creek Flood Plain

500 ft

NDVI = NIR- Red / NIR + Red

How to translate NDVI readings into N rate recommendations?

Nitrogen Algorithm(s)

One of the first modern applications of remote sensing and it’s use…

to determine N rates by estimating yield using NDVI

NDVI provides an estimate of above ground biomass

First Nitrogen Application Algorithms were derived from yield estimates using remote sensing

Big turning point in the history of data‐driven N management

Raun et al 2001. In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy reflectance

Cumulative Growing Degree Days (GDD)

Abo

ve G

roun

d B

iom

ass

NDVI Time 1

NDVI Time 2

Expected Yield (EY) = (NDVI T1 + NDVI T2) / GDD(INSEY)

• In 2002, Raun et al., developed the Nitrogen Fertilization Optimization Algorithm (NFOA)

• a multi-step process:

1. Generate Yield Prediction Equation (YP0) from the INSEY and previous year’s yield data

2. Field data collection for N response


Raun et al 2002 Improving NUE in Cereal Grain Production with Optical Sensing and Variable Rate Application

Generate Yield Prediction Equation (YP0)

INSEY

Gra

in Y

ield

(kg

/ha)

YPo= a X e(b X INSEY)

8/1/2017


N-Rate Field Experiment

Collect sensor readings

Collect temperature data for GDD


NDVI 0.85

NDVI 0.59

NDVI 0.73

Response Index (RI) = NDVIRich / NDVIReferencePotential for yield increase ~44%

with additional NRI= .85 / .59 = 1.44

RI= .85 / .73 = 1.16 Potential for yield increase ~16% with additional N

How much additional N?

3. Calculate Yield Potential with added N fertilizer (YPN)

YPN = YP0 * RI

4. Compute Grain N uptake at YP0 & YPN

GNUP_YP0 = YP0 x % N Grain

GNUP_YPN = YPN x % N Grain

5. Final N Rate = (GNUP_YPN – GNUP_YP0) / NUE


Raun et al 2002 Improving NUE in Cereal Grain Production with Optical Sensing and Variable Rate Application

Limitations:

I. NDVI saturates at high LAI values

II. This algorithm does not account for location of plant in the field

VEGETATION INDICES EQUATION

Normalized Green Index (GRI)

Normalized Red Edge Index (NREI)

Normalized Difference Red Edge Index (NDREI)

Green Chlorophyll Index (GRI)

Red Edge Chlorophyll Index (RECI)

Green Soil Adjusted Vegetation Index (GSAVI)

Green Optimal Soil Adjusted Vegetation Index (GOSAVI)

Modified Chlorophyll Absorption in Reflectance Index

G/(NIR+RE+G)

G/NIR+RE+G

RE/(NIR + RE + G)

RE/(NIR + RE + G)

(NIR – RE)/(NIR + RE)

(NIR – RE)/(NIR + RE)

NIR/G‐1

NIR/RE‐1

1.5 * [(NIR – RE)/(NIR + RE +.5)]

(1 + .16)(NIR – G)(NIR + G + .16)

[(NIR – RE) ‐ .2 *(NIR – G)]/(NIR/RE)

Cao et al 2014: Active Canopy Sensing of Winter Wheat Nitrogen Status: An evaluation of two Sensor Systems

New Vegetation Indices to Detect N StatusVEGETATION INDICES Equation

Normalized Green Index G/(NIR + RE +G)

Normalized Red Edge Index RE/(NIR + RE +G)

Normalized NIR Index NIR/(NIR + RE +G)

Red Edge Ratio Vegetation Index NIR/RE

Green Ratio Vegetation Index NIR/G

Red Edge Green Ratio Vegetation Index RE/G

Green Difference Vegetation Index NIR‐G

Red Edge Difference Vegetation Index RE‐G

Normalized Difference Red Edge (NIR‐RE)/(NIR+RE)

Green Normalized Difference Vegetation Index (NIR‐G)/(NIR+G)

Red Edge GNDVI (RE‐G)/(RE+G)

Green Wide Dynamic Range Vegetation Index (a*NIR‐G)/(a*NIR+G)(a‐.12)

Red Edge Wide Dynamic Range Vegetation Index (a*NIR‐RE)/(a*NIR + RE)(a‐.12)Optimized Vegetation Index 1 100*(lnNIR‐lnRE)

Modified Double Difference Index (NIR‐RE)‐(RE‐G)

Modified Normalized Difference Index (NIR‐RE)/(NIR‐G)

Green Chlorophyll Index NIR/G‐1

Red Edge Chlorophyll Index NIR/RE‐1

Modified Red Edge Simple Ratio (NIR/RE‐1/SQRT(NIR/RE+1)

Modified Green Simple Ratio (NIR/G‐1)/SQRT(NIR/RE+1)

Modified Enhanced Vegetation Index 2.5* (NIR‐RE/(NIR+6*RE‐.75*G+1)

Modified Normalized Difference Red Edge [NIR‐(RE‐2*G)]/[NIR+(RE‐2*G)]

Modified Chlorophyll Absorption in Redlectance Index [(NIR‐RE)‐.2*(NIR‐G)](NIR/RE)

Modified Transformed CARI 3*[(NIR‐RE)‐.2*(NIR‐G)(NIR/RE)]

Green Soil Adjusted Vegetation Index 1.5*[(NIR‐G)/(NIR+G+.5)]

Red Edge Soil Adjusted Vegetation Index 1.5*[(NIR‐RE/(NIR+RE+.5)]

Green Optimal Soil Adjusted Vegetation Index (1+.16)(NIR‐G)/(NIR+G+.16)

Red Edge Optimal Soil Adjusted Vegetation Index (1+.16)(NIR‐RE)/(NIR+RE+.16)

Red Edge Transformed Vegetation Index .5[120*(NIR‐G)‐200*(RE‐G)]

Grenn re‐Normalized Difference Vegetation Index (NIRE‐RE)/SQRT(NIR+RE)

Limitations:

NDVI saturates at high LAI values

II. Accounting for location of plant in field

8/1/2017


N Rate (kg ha-1) = (135.3 x (NDVIRef. / NDVITarget)2) – (134.8 x (NDVIRef. / NDVITarget)) + 1

~96 lbs/a

~96 lbs/a

~96 lbs/a

NDVI

0.41

NDVI

0.41

NDVI

0.41

Coupling site-specific management zones with active proximal sensors

N Rate (kg ha-1) = (135.3 x (NDVIRef. / NDVITarget)2) – (134.8 x (NDVIRef. / NDVITarget)) + 1

~92 lbs/a

~144 lbs/a

~37 lbs/a

High

Medium

N Rate (kg ha-1) = Crop properties + Soil Properties

NDVI

0.41

NDVI

0.41

NDVI

0.41

Low

Crop Based Management

Micro-variabilityMacro-variability

High MZ Medium MZ Low MZ

N management strategies

High Medium Low

UniformRemote Sensing

0 kg

/ha

112

kg/h

a

224

kg/h

a

0 kg

/ha

112

kg/h

a

224

kg/h

a

0 kg

/ha

112

kg/h

a

224

kg/h

a

Management Zones

224 kg/ha168 kg/ha

112 kg/ha

Remote sensing within Management Zones

112

kg/h

a

168

kg/h

a

224

kg/h

a

56 k

g/ha

112

kg/h

a

168

kg/h

a

0 kg

/ha

56 k

g/ha

112

kg/h

a

N management strategies

224 kg/ha

High Medium Low

8/1/2017


150

120

90

60

30

0

d

c

b a

d

c

ba

NU

Ea

(kg

Gra

in /

kg N

)

d

c

ba

NUEa

2010 2011 2012

Uniform MZ RS RS + MZ

224

168

112

56

0

N a

pplie

d (k

g/ha

)

Improvement in NUE and reductions in N loadings in the biosphere.

Uniform MZ RS RS + MZ0

28

Dif

fere

nce

in

N a

pp

lied

(kg

/ha)

N loadings Uniform MZ RS RS + MZ

15

10

5

0

a a aa

a a

ba

Yie

ld (

Mg/

ha)

Yield

a a aa


2010 2011 2012

Dif

fere

nce

in N

2O e

mis

sion

(k

g/h

a/y)

0

0 75


2010 2011 2012

6.0

4.5

2.0

1.5

0

N2O

em

issi

on (

kg/h

a/y)

N2O emissions

-54%

-55%-50%

Reductions in N2O linked to fertilizer

8/1/2017


There will be even more complex soil and crop models that encompass many other sensitive parameters

Machine learning

Model Time ScaleDaily time-step; historical weather data to predict N flux

Weather InputsReal-time; solar radiation; temperature; precipitation

Soil InputsNRCS SSURGO; root depth; slope; SOM; drainage

Cultivar; maturity class; population; yieldCrop Inputs

Management Inputs Tillage; manure; previous crop characteristics

N Fertilizer Inputs Type; rate; timing; pricing

N Rate OutputMass balance; deterministic and stochastic; price risk factors

Graphical OutputN loss and uptake; N dynamics; crop development; fertilizer maps

Method Approach

Sella et al 2016 Adapt-N Outperforms Grower-Selected Nitrogen Rates in Northeast and Midwestern United States Strip Trials,

N Fertilizer Inputs

Management Inputs

Graphical Output

N Rate Output

Nrec=Nexp_yld - Ncrop_now – Nsoil_now

- Nrot_credit – Nfut_gain – loss - Nprofit_risk

8/1/2017


Increasing NUE with advanced decision making process

N2O

N

Yield

Thank [email protected]

khosla presentation info ag 2017 handout...8/1/2017 prof. r. khosla, colorado state university 2...

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