precision-water, -nitrogen and -seed management for ... fertilizer forum february 2016 raj khosla,...

Fluid Fertilizer Forum February 2016

Raj Khosla, Jose Chavez, Allan Andales, Robin Reich

and Louis Longchamps

Colorado State University

Precision-Water, -Nitrogen and -Seed

Management for Enhancing Efficiency,

Productivity, and Profitability of Irrigated

Cropping Systems

Basic agronomy

Law of limiting factors

N prescription algorithm

𝑁 = 35 + 1.2 𝑙𝑏𝑠 𝑁 × (𝐸𝑌)

Accounting for yield

𝑁 = 35 + [1.2 𝑙𝑏𝑠 𝑁 × 𝐸𝑌 ] − 8 × 𝑝𝑝𝑚 𝑁𝑂3𝑁 − 0.14 × 𝐸𝑌 ×%𝑂𝑀

Accounting for yield and soil N

𝑁 = 35 + [1.2 𝑙𝑏𝑠 𝑁 × 𝐸𝑌 ] − 𝑆𝑜𝑖𝑙 𝑁 × 𝑅𝑖𝑐ℎ𝑆𝑡𝑟𝑖𝑝𝑁𝐷𝑉𝐼 𝑇𝑎𝑟𝑔𝑒𝑡𝑁𝐷𝑉𝐼

Accounting for yield, soil N and crop status

𝑁 = 35 + [1.2 𝑙𝑏𝑠 𝑁 × 𝐸𝑌 ] − 𝑆𝑜𝑖𝑙 𝑁 ÷ 𝑁𝐷𝑉𝐼 × 𝑆𝑒𝑒𝑑 𝑅𝑎𝑡𝑒 × 𝐼𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛

Accounting for yield, soil N, crop status and seed and irrigation rate

Water Fertilizer

Seeds

VR Planting

Crop sensing

MZ

Precision water

management

1. Quantify spatial and temporal variability in soil water balance across

the 22 acre precision pivot equipped field

2. Develop early season (corn growth stage V4-V6) in-season precision

nitrogen management system for irrigated corn.

3. Evaluate variable rate seeding in conjunction with variably managed

water and nutrient crop field.

Objectives of the project

I. Water Management

But that field

is flat!

Is there spatial variability?

A

Sand: 39%

Silt: 55%

Clay: 6%

B

5%

75%

20%

System dating

to 1905

6”

18”

30”

42”

54”

June July Aug. Sept.

30”

18”

6”

42 ”

54”

Example

Spatial and Temporal variability

• There is significant spatial variability

• There is significant temporal variability in soil moisture at the field

scale across the crop growing season.

• The temporal range of soil moisture is fairly large in depth and

shorter at the surface

Soil water

9.14.2012

Clip6_1_2012

<VALUE>

0 - 8 484

8 485 - 9 000

9 001 - 10 000

10 010 - 11 000

11 010 - 12 000

12 010 - 13 000

0 – 0.27

0.27 – 0.30

0.30 – 0.36 (m3/m3)

0.36 – 0.42

0.42 – 0.48

> 0.48

1. Quantifying variability of soil water content

Practical implications

• Spatial variability in soil water may justify a Variable-Rate Irrigation

• Spatial range of variability is conducive to a management zones approach

• Water management zones delineated using data from deeper soil profile

are more stable in time

• Top soil is harder to map and uniform rate may be better at early stages

when roots are in the top soil (our hypothesis)


2. Early season N management using remote sensing

Management zones

(macro-management)

Active remote-sensing

(micro-management)

Coupling site-specific management zones

with active proximal sensors

Attempt to manage both, macro- and micro-variability in farm-fields

Macro-variability

Micro-variability

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

~96 lb/A

~96 lb/A

~96 lb/A

NDVI

0.41

NDVI

0.41

NDVI

0.41

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

~92 lb/A

~144 lb/A

~37 lb/A

High

Medium

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

NDVI

0.41

NDVI

0.41

NDVI

0.41

Low

Crop Sensing + Soil Sensing

Make better and most efficient nutrient

management decisions


Micro-variability Macro-variability

High MZ Medium MZ Low MZ

N management strategies

High Medium Low


Uniform Remote Sensing

0 l

bs/

A

10

0 l

bs/

A

20

0 l

bs/

A

0 l

bs/

A

10

0 l

bs/

A

20

0 l

bs/

A

0 l

bs/

A

10

0 l

bs/

A

20

0 l

bs/

A

Management Zones

200 lbs/A

150 lbs/A

100 lbs/A

Remote sensing within Management Zones

10

0 l

bs/

A

15

0 l

bs/

A

20

0 l

bs/

A

50

lb

s/A

10

0 l

bs/

A

15

0 l

bs/

A

0 l

bs/

A

50

lb

s/A

10

0 l

bs/

A

200 lbs/A

High Medium Low


240

160

80

0

a a a a

a a

b

a Y

ield

(b

u/A

)

Yield

a a a a

Uniform MZ RS RS + MZ

Site Yr 1 Site Yr 2 Site Yr 3

150

120

90

60

30

0

d

c

b a

d

c

b

a

NU

Ea

(lbs

Gra

in /

lbs

N a

ppli

ed)

d

c

b a

NUEa Uniform MZ RS RS + MZ

Site Yr 3 Site Yr 1 Site Yr 2

Amber and Red NDVI correlation with nitrogen application rates across site years.

V6 0.61 0.57 0.45 0.44

V8 0.89 0.82 0.62 0.62

V10 0.81 0.80 0.83 0.77

V12 0.90 0.87 0.95 0.88

V14 0.92 0.89 0.86 0.82

Site Year 1 Site Year 2 Corn Amber NDVI Red NDVI Amber NDVI Red NDVI Growth Stage ------------------------ Linear Plateau R2 -----------------------------

V2 V4 V6 V9 V12

28”

44”

28”

44”

Mulitplex® Fluorescence Sensor

Greenhouse experiment with fluorescence

NB

I_R

m

0

2

4

6

8 V4 V5 V6 V7 V8 V9

N Rate (kg/ha)

0

75

150

225

0

75

150

225

0

75

150

225

0

75

150

225

0

75

150

225

0

75

150

225

Greenhouse experiment with fluorescence

Experiment in the field

V4 V5 V6 V7 V8 V9

N Rate (kg/ha)

0

75

150

225

0

75

150

225

0

75

150

225

0

75

150

225

0

75

150

225

0

75

150

225

Experiment in the field N

BI_

Rm

0

2

4

6

8

N f

erti

lize

r

N f

erti

lize

r N f

erti

lize

r N f

erti

lize

r N f

erti

lize

r

Planting 5 seed rates

3. Variable rate seeding in conjunction with water and N

Hybrid: DKC-4620 @ 30”

Planting date: May 15th 2013

Harvest date: Oct. 24th 2013

2013


Planting date: Apr 29th 2014

Harvest date: Oct. 30th 2014

2014

Location: CSU Research farm (ARDEC), Fort Collins, CO

For all years, 250 lbs N/ac was supplied and irrigation was supplied

based on crop ET


Planting date: May 27th 2015

Harvest date: Nov. 19th 2015

2015

2013

0 10000 20000 30000 40000 50000

05

01

00

15

02

00

Plant population (plants/ac)

Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

Lower yield Medium yield Higher yield

0 10000 20000 30000 40000 50000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000 60000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000 60000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000 60000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000 60000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000 60000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000 60000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

0 10000 20000 30000 40000 50000 60000

05

01

00

15

02

00


Yie

ld (

bu

/ac)

2014

Data from Pioneer Crop Insights

Source: Doerge, T., M. Jeschke, and P. Carter. 2015. Planting Outcome Effects on Corn Yield.

Crop Insights 25(1): 1–7.

Water Fertilizer

Seeds

!A

20

20

20

20

34 34

41 41

48 48

27 27

48 48

41 34

27 27

20 41

34 34

34

41

48

27

41

48

34

27

27

48

41

34

41

48

34

27

41

48

34

27

0 90 18045 Feet

¯

!A

20

20

20

20

34 34

41 41

48 48

27 27

48 48

41 34

27 27

20 41

34 34

34

41

48

27

41

48

34

27

27

48

41

34

41

48

34

27

41

48

34

27

0 90 18045 Feet

¯

N rates (lbs N/A)

0

50

100

150

200

80 % ET 80 % ET

2015


Seed rate Irrigation rate


Seed rate N rate


Increased seed rate should be

accompanied with increased input rates

Questions:

How to integrate effect of soil properties on seed rate?

How to optimize seed, N, and water for each soil type?

Could we develop an N algorithm that incorporates

irrigation and seed rates as well as soil properties

and yield goal?

What is the effect of timing on these interactions?

What is the…..?

Raj Khosla, Jose Chavez, Allan Andales, Robin Reich and Louis Longchamps

Colorado State University

Thank you [email protected]

mailto:[email protected]

precision-water, -nitrogen and -seed management for ... fertilizer forum february 2016 raj khosla,...

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