Using Crop Sensor Technology to Replace Conventional … · 2017. 4. 13. · • Increased yields realized from planting earlier • Earlier planted soybean lead to increase yields

Using Crop Sensor Technology to Replace Traditional Measurements for Evaluating

Seed Treatment Efficacy in Corn and Soybeans.

4/17/2014

Background • Raised in western Iowa corn, soybean, hay, and livestock

farm • B.S. in agronomy from Northwest Missouri State

University • Worked 4 years as a crop consultant in SW Kansas • 3 years in Central Iowa as an agronomist at a coop • DuPont Pioneer

• 5 years in soybean research • Last 4 years in seed treatment testing

My wife, Kari and our 3 kids, Sam, Anna, and Will live in Adel, IA

Introduction • Purpose

• Can crop sensing technology can replace measurements such as stand count, early growth measurements, plant uniformity and yield?

• Currently used measurements are time consuming, some are subjective, and require multiple field visits

• Importance of seed treatments • Virtually all corn sold is treated • Over 50% of the soybeans now sold are treated

(Esker and Conley, 2012)

• Planting date moved approximately 2 weeks earlier since mid 1980s (Kucharik, 2006)

• Cooler wetter soils • Slower emergence

Introduction (cont.) • Slower emergence

• Increase time for seed and seedling diseases to affect stand and stand uniformity

• Why are growers planting earlier? • Increased yields realized from planting earlier

• Earlier planted soybean lead to increase yields (Bastidas et al., 2008, and Rowntree, 2013)

• Part of the yield increase in corn since 1979 due to earlier planting date (Kucharik, 2008)

• Growers using larger equipment - Plant more acres in shorter period of time

• Increase need for seed treatment to aid in stand establishment of both corn and soybean

• Seed treatments must protect the seed and seedling from more diseases and insects for a longer period of time while the seedling plant emerges

Seed Treatment History • Seed treatments use first documented over 2000 years ago (FIS, 1999)

• First treatments were plant extracts • Potato famine in Ireland in 1840 and 1845 spurred along

development of plant and seed protectants (Russell, 2005)

• Late 1800s sulfur, copper, and lime were used to control fungi (Russell, 2005)

• WWII stimulated significant advances in the chemical industry (Russell, 2005)

• After the war some of the industry shifted towards the crop protections industry

• Some chemicals developed during this time are still in use today as seed treatment

Seed Treatment History (cont.) • According to Transparency Market Research (2013) the

seed treatment market in 2011 valued at 2.43 billion USD • Projected to increase to 4.45 billion USD in 2018 • Corn and soybean largest market for seed treatments • Recent increase in use and demand for seed treatments

prompted chemical companies to focus effort on seed treatments • Early development of seed treatments often were foliar fungicide

re-purposed for use as seed treatments • More seed treatment products will be available each year • Critical for seed companies to evaluate products and product

combinations to determine best available technology

Current seed treatment • Many current seed treatment offers include:

• Fungicides – focus are to control the Oomycetes (Pythium spp. and Phytophthora spp.), Fusarium spp., and Rhizoctonia solani

• Insecticide – control early season above and below ground insects

• Biological – use bacteria, fungi, or other microorganism and/or non-pesticidal compound that provide some protection against seedling diseases or insects (Munkvold, 2009)

• Colorants – ensure seed does not enter food or feed channels, also may contain surfactants to aid in mixing and application of products

• Polymers – ensure applied chemicals adhere to seed and reduces seed treatment “dust off”

Current seed treatment • With current seed treatment

application technologies high rate seed treatments are near the maximum amount of seed treatment that can be applied to seed

• New development of seed treatment application technologies likely will lead to increased loading capability

Multispectral History • Remote sensing methods used since 1930’s (Nellis et

al. 2009)

• Initial efforts primarily by U.S. Soil Conservation Service to complete soil surveys

• U.S. Department of Agriculture to estimate crop areas • Infrared photographs used during WWII allowed to

some extent crop condition estimates • In the 1970s Landsat satellite used to estimate wheat

yields • More recent advances resulted in the development of

crop sensing technology that is able to attach to farm equipment or be handheld

Methods and Materials – Plot information

• Blue border represents entire 4 row x 17.5’ plot and the red lines mark the 2 center rows of the plot used for all data collection.

• 2011 experiments contained 6 reps

• 2012 experiments included 4 reps

• Seed treatment experiments included from 9-12 seed treatments

• All experiments are planted using the randomized complete block design

Methods and Materials - Locations

2011 and 2012 corn and soybean locations (USDA-NCSS, 2008 and Google Earth,

2013). 2011 Corn Locations 2012 Corn Locations 2011 Soybean

Locations 2012 Soybean

Locations

Alleman, IA Johnston, IA Johnston, IA Lacona, IA Lacona, IA Alleman, IA Johnston, IA Minburn, IA Lacona, IA

Alleman (2011) – Clarion = Fine-loamy, mixed, superactive, mesic Typic Hapludolls & Nicollet = Fine-loamy, mixed, superactive, mesic Aquic Hapludolls

Alleman (2012) - Nicollet = Fine-loamy, mixed, superactive, mesic Aquic Hapludolls & Webster = Fine-loamy, mixed, superactive, mesic Typic Endoaquolls

Johnston – Wiota = Fine,silty, mixed, mesic Typic Argiudolls Minburn – Canisteo = Fine-loamy, mixed, superactive, calcareous, mesic Typic Endoaquolls

Lacona – Winterset = Fine, Montmorillonitic, mesic, Typic Argiaquolls

Methods and Materials - Crop Sensor ACS-430 Sensor – used in 2011

ACS-470 Sensor – used in 2012

Crop Circle™ - how do they work? • Three wavelengths of light emitted (Holland 2010)

• 670 nanometers (visible red) • 730 nanometers (red edge) • 780 nanometers (near infrared)

• Normalized Difference Vegetative Index (NDVI) equation

= 780(𝑁𝑁𝑁)−670(𝑁𝑅𝑅)780(𝑁𝑁𝑁)+670(𝑁𝑅𝑅)

• Many other indices could be used from the 3 reflectance

measurements taken

TM Crop Circle is a trademark of Holland Scientific, Lincoln, Nebraska.

Why these 2 Wavelengths?

Carter and Knapp, 2001. Simulation of wavelength reflectance by chlorophyll at 6 different concentrations measured on filter pads to simulate leaf chlorophyll.

670 nm and 780 nm wavelengths used in this study

Key Spectral Regions Spectral Region

Wavelength (nm) Significance

Green 520,560 Green band peak or the point of maximal reflectance in the visible spectrum. (Thenkabail et al. 2000)

Red 660,690

Absorption maxima. Maximum chlorophyll Absorptions Greatest soil crop contrast, Sensitive to biomass and LAI (670-680) (Thenkabail et al. 2004).680 nm is responsible for chlorophyll and pigment estimation (Blackburn, 1998a)

Red Edge 730

Plant stress is the best detected at the red-edge bands centered around the 705 nm and 735 nm (Elvidge and Chen, 1995, Thenkabail et al. 1999)

NIR 760, 780, 790, 800

Early NIR. More sensitive to changes in chlorophyll content than a broad NIR band. (Thenkabail et al. 2000)

Summary of spectral regions commonly used in sensor measurements and their significance in plant measurements (modified from Ray, Singh, and Panigrahy, 2010).

Photosynthetically Active Radiation

Electromagnetic spectrum with the PAR spectrum magnified (Casady and Palm,

2002)

Corn -Traditional Measurment and comparative NDVI Measurement

Current Trait NDVI Measurement Comparison Description Growth

Stage Comments Growth

Stage Statistical

Measurement used

Comments

Early stand count (ESTCNT)

V2-V4 Number of plants per center 2 rows of a 4 row plot

1) V1-V2* 2) V4-V6 3)V8-V9

Mean NDVI Readings were taken on center 2 rows of a 4 row plot

Early Growth (EGRWTH)

V2-V4 Visual 1-9 score for early growth at the 2-4 leaf stage.

1) V1-V2* 2) V4-V6 3)V8-V9


Early Runt count (ERNTCT)

V4-V6 Number of plants in center 2 rows of plot that are 2 or more leaves behind

1) V1-V2* 2) V4-V6 3)V8-V9

Coefficient of Variation

Readings were taken on center 2 rows of a 4 row plot

Grain yield (Yield)

Harvest Center 2 rows are harvested for yield, calculations based upon 56 pounds/bushel @ 15% moisture

1) V1-V2* 2) V4-V6 3)V8-V9


* Measurement not taken in 2012

Current seed treatment measurements • Runt count – ERNTCT

• Count of all plants in the center 2 rows of a 4 row plot that are 2 or more leaf collars behind the rest of the plants in a plot.

• Converted to a percentage for analysis

• Especially critical measurement when cool wet conditions occur after planting but before emergence

• For every 2-leaf stage delay in emergence decreases yield of that plant by 36% (Liu et al. 2004)

Soybean Traditional Trait – NDVI Measurement

Current Trait NDVI Measurement

Comparison Description Growth

Stage Comments Growth

Stage Statistical

Measurement used

Comments

Seedling Vigor (SDVIG)

VC Visual 1-9 score for early growth at the VC growth stage.

1) V2-V3 2) V4 or V8-V9 3)R1-R2

Mean NDVI Readings taken on center 2 rows of a 4 row plot at 3 different growth stages

Early stand count (STDCNT)

V1-V2 Number of plants In 10’ of row in either of the 2 harvest rows

1) V2-V3 2) V4 or V8-V9 3)R1-R2


Grain yield (Yield)

Harvest Center 2 rows are harvested for yield, calculations based upon 60 pounds/bushel @ 13% moisture

1) V2-V3 2) V4 or V8-V9 3)R1-R2


Seedling Vigor Score Seedling Vigor Score (SDVIG) (DuPont Pioneer, 2011)

Objective: To determine comparatively how fast the seedling/plants are developing. Timing: Score when 5% of plots would score a 9 for SDVIG (VC stage). Scale for assigning scores: Description

9 >90% of the plants growing have expanded unifoliates

8 >80% and <90% of the plants growing have expanded unifoliates






2 <20% of the plants growing have expanded unifoliates 1 none of the plants growing have expanded unifoliates

Current seed treatment measurements - soybeans

• Stand count • Count of plants per 10’ of

row in one of the 2 harvest rows

• Evaluation completed at V1-V2 growth stage

• Critical assessment for seed treatment efficacy

Seed treatment difference

No Treatment

Full seed treatment package

Methods and Materials – Corn Hybrids

Location Year Hybrid Traits

Alleman, IA 2011 P1162XR HXX,LL,RR2

Lacona, IA 2011 P1395XR HXX,LL,RR2

Minburn, IA 2011 P1162XR HXX,LL,RR2

Johnston, IA 2012 P1395XR HXX,LL,RR2

Alleman, IA 2012 P1395XR HXX,LL,RR2

Lacona, IA 2012 P1395XR HXX,LL,RR2

Corn hybrids used for seed treatment trials in 2011 and 2012.

• All hybrids and locations were planted at 34,000 seeds per acre

Methods and Materials – Soybean Varieties

Soybean Varieties used for seed treatment trials in 2011 and 2012. Location Year Variety Traits

Johnston, IA 2011 93Y11 RR/SCN 93Y13 RR/SCN

Johnston, IA 2012

93Y05 RR/SCN 93Y15 RR/SCN 92Y82 RR 93M11 RR 92Y80 RR/SCN 93Y11 RR/SCN

Lacona, IA 2012

93Y05 RR/SCN 93Y15 RR/SCN 92Y82 RR 93M11 RR 92Y80 RR/SCN 93Y11 RR/SCN

• All varieties were planted at 140,000 seeds per acre or about 8 per foot.

Analysis of Variance Current Measurements NDVI Measurements

Year Location Independent Variable DF

ESTCNT Pr>F

EGRWTH Pr>F

YIELD Pr>F

NDVI Mean (V1-V2) Pr>F



2011 Alleman Treatment 54 0.5995 0.6267 0.2673 0.6021 0.4843 0.0715

2011 Lacona Treatment 55 0.6027 0.5414 0.2320 0.7111 0.7162 0.7228

2011 Minburn Treatment 55 0.0606 0.3331 0.8532 0.6841 0.2863 0.4341

2012 Johnston Treatment 27 0.0809 0.4326 0.1904 - 0.0188 0.0123

2012 Alleman Treatment 27 0.0524 <.0001 0.0192 - 0.0102 0.006

2012 Lacona Treatment 11 0.3685 0.266 0.1997 - 0.166 0.1359 Trait Definitions: ESTCNT – Early stand count of all seedlings in the center 2 rows of a 4 row plot EGRWTH – Early growth rating of corn plants taken at V2-V4 growth stage: 1-9 scale (1 = poor, 9 = excellent) YIELD – grain yield measured in the center 2 rows of the 4 row plot at 56 pounds per bushel and 15% moisture

ANOVA performed in SAS (SAS Enterprise Guide, 2012)

Corn - Early Stand Count

52

54

56

58

60

62

64

66

ESTC

NT

0.25 0.275 0.3 0.325 0.35 0.375 0.4

NDVI_Mean (V4-V6)

R2=.29 Y=42.796621 + 52.271494*X P=0.0004

52

54

56

58

60

62

64

66

ESTC

NT

0.55 0.6 0.65 0.7

NDVI_Mean (V8-V9)

R2=.28 Y=29.851314 + 45.684003*X P=0.0004

Regressions performed in JMP, SAS Institute 2013

Corn - Early Growth evaluations

2

3

4

5

6

7

8

9

EGR

WTH

0.25 0.275 0.3 0.325 0.35 0.375 0.4

NDVI_Mean (V4-V6)

2

3

4

5

6

7

8

9

EGR

WTH

0.55 0.6 0.65 0.7

NDVI_Mean (V8-V9)

R2=.20 Y=0.0209655 + 19.859338*X P=0.0042

R2=.23 Y=-5.93253 + 18.933086*X P=0.0018


Corn - Yield

190

200

210

220

230

240

250

260

270

280

YIE

LD (b

u/a

56#)

0.25 0.275 0.3 0.325 0.35 0.375 0.4

NDVI_Mean (V4-V6)

19

20

21

22

23

24

25

26

27

28

YIE

LD (b

u/a

56#)

0.55 0.6 0.65 0.7

NDVI_Mean (V8-V9)

R2=0.53 Y=112.13094 + 380.95168*X P<.0001

R2=0.42 Y=-40.341891 + 298.59323*X P<.0001


Early Stand Count, Early Growth, and Yield compared to NDVI Measurements Current Measurements NDVI Measurements

Year Location Treatment ESTCNT EGRWTH YIELD

NDVI Mean (V1-

V2)

NDVI Mean (V4-

V6) NDVI Mean

(V8-V9) 2012 Alleman 1 55.8b 3.8b 221.7c - 0.2798b 0.6001b

2 61.8a 7.3a 250.0a - 0.3494a 0.6793a

3 60.8a 6.8a 225.4c - 0.3152a 0.6541a

4 61.8a 6.3a 250.8a - 0.3416a 0.6747a

5 61.0a 6.5a 245.9ab - 0.3319a 0.6684a

6 59.3ab 6.8a 237.1abc - 0.3254a 0.6592a

7 60.3a 6.3a 231.4abc - 0.3280a 0.6656a

8 59.8a 7.0a 234.2abc - 0.3127a 0.6434a

9 59.8a 7.3a 228.2bc - 0.3366a 0.6470a

10 58.5ab 7.3a 239.5abc - 0.3419a 0.6748a

Duncan’s Multiple Range Test Performed in SAS (SAS Enterprise Guide, 2012)

Plant uniformity compared to NDVI Measurements

Current Measurement NDVI Measurement

Year Location Independent Variable DF ERNTCT% Pr>F

NDVI CV

(V1-V2) Pr>F

NDVI CV

(V4-V6) Pr>F

NDVI CV

(V8-V9) Pr>F

2011 Alleman Treatment 54 0.6551 0.8414 0.5765 0.4375

2011 Lacona Treatment 55 0.0924 0.7186 0.6759 0.6246

2011 Minburn Treatment 55 0.8638 0.3681 0.1156 0.0226

2012 Johnston Treatment 27 0.5840 - 0.2248 0.0214

2012 Alleman Treatment 27 0.8810 - 0.1571 0.0504

2012 Lacona Treatment 11 0.3391 - 0.3010 0.4832

Trait Definitions: ERNTCT – Number of plants in the two center rows of a 4 row plot that are at least 2 leaf collars behind the average leaf collar count of the plot. This trait is measured between V4 and V6. NDVI CV – Coefficient of variation at V1-V2, V4-V6, and V8-V9 growth stages respectively.


Plant Uniformity relationship to NDVI – CV

-2

0

2

4

6

8

10

12

14

ERN

TCT%

0.06 0.08 0.1 0.12 0.14

NDVI_CV (V1-V2)

-2

0

2

4

6

8

10

12

14

ERN

TCT%

0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0

NDVI_CV( V4-V6)

-2

0

2

4

6

8

10

12

14

ERN

TCT%

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0

NDVI_CV (V8-V9)

R2 =.04 Y=-0.015961 + 24.651466*X P=.1056

R2 =.06 Y=-1.498906 + 24.098718*X P=.0422

R2 =.01 Y=1.5174924 + 22.938856*X P=.3603

Plant Uniformity - NDVI by Treatment

Year Location Treatment ERNTCT% NDVI CV (V1-

V2) NDVI CV (V4-

V6) NDVI CV (V8-

V9) 2011 Lacona 1 0.8c 0.0866a 0.1635a 0.0247a

2 1.7bc 0.0833a 0.1574a 0.0304a

3 1.7bc 0.0882a 0.1387a 0.0321a

4 2.0abc 0.0881a 0.1529a 0.0340a

5 4.2ab 0.1011a 0.1659a 0.0220a

6 1.8abc 0.0946a 0.1508a 0.0258a

7 0.7c 0.0991a 0.1647a 0.0311a

8 5.0a 0.0994a 0.1602a 0.0334a

9 2.7abc 0.0927a 0.1473a 0.0324a

10 2.7abc 0.0857a 0.1534a 0.0373a

11 1.8abc 0.0807a 0.1485a 0.0318a

12 1.5bc 0.0832a 0.1427a 0.0264a

Trait definitions: ERNTCT – number of plants 2 or more leaf collars behind at ~ V5 growth stage. Lower number is better.


Analysis of Variance Current Measurements NDVI Measurements

Year Location - Experiment Independent Variable DF SDVIG Pr>F STDCNT Pr>F

YIELD Pr>F

NDVI Mean

(V2-V3) Pr>F

NDVI Mean (V4)

Pr>F

NDVI Mean (V8-V9)

Pr>F

NDVI Mean

(R1-R2) Pr>F

2011 Johnston - Experiment #1 Treatment 85 <.0001 <.0001 0.0508 <.0001 - 0.0004 0.5564

Variety 85 0.0126 0.0979 0.8045 0.0003 - <.0001 0.2727

Treatment*Variety 85 0.1451 0.6867 0.2997 0.4542 - 0.3378 0.6327

2012 Johnston - Experiment #1 Treatment 56 <.0001 0.0185 0.2828 0.4578 0.0003 - 0.0289

Variety 56 <.0001 0.0158 <.0001 0.1958 0.3866 - 0.8922

Treatment*Variety 56 0.4894 0.2208 0.2484 0.7316 0.4857 - 0.8409

2012 Johnston - Experiment #2 Treatment 57 0.0549 0.8251 0.0604 0.3018 0.072 - 0.3497

Variety 57 <.0001 <.0001 <.0001 0.0265 0.0498 - 0.0109


2012 Johnston - Experiment #3 Treatment 63 <.0001 0.0353 0.4416 0.0024 <.0001 - 0.0004

Variety 63 <.0001 0.0041 <.0001 0.0007 0.0086 - 0.4387


2012 Lacona - Experiment #1 Treatment 56 0.1169 0.1905 0.6283 0.0097 - 0.0011 0.2926

Variety 56 0.0924 0.1884 <.0001 0.1534 - 0.0613 0.0438


2012 Lacona - Experiment #2 Treatment 57 0.0959 0.0901 0.2924 0.0121 - 0.1193 0.8785

Variety 57 0.3119 <.0001 <.0001 <.0001 - 0.0324 <.0001


2012 Lacona - Experiment #3 Treatment 62 0.0008 0.497 0.1092 <.0001 - 0.0014 0.0272

Variety 62 0.2474 0.8215 <.0001 0.0008 - 0.0075 0.0461


Trait Definitions: SDVIG – seedling vigor measured with a 1-9 scale (1 = poor vigor, 9 = excellent vigor) at VC growth stage STDCNT – number of plants in 10’ of row in one of the center 2 rows of the plot YIELD – grain yield measured in the center 2 rows of the 4 row plot at 60 pounds per bushel and 13% moisture


Seedling Vigor – NDVI Regressions

1

2

3

4

5

6

7

8

9

10

SDV

IG

0.2 0.22 0.24 0.26 0.28 0.3 0.32 0

NDVI_MEAN (V2-V3)

1

2

3

4

5

6

7

8

9

10

SDV

IG

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8

NDVI_MEAN (V8-V9)

1

2

3

4

5

6

7

8

9

10

SDV

IG

0.835 0.84 0.845 0.85 0.855 0.86 0.865 0.87 0.875 0

NDVI_MEAN (R1-R2)

R2=.80 Y= -9.155088 + 57.362756*X P<.0001

R2=.66 Y= -5.83087 + 19.156932*X P<.0001

R2=.14 Y= -70.7491 + 90.437062*X P<.0074


Stand Count – NDVI Regressions

20

30

40

50

60

70

80

90

STD

CN

T

0.2 0.22 0.24 0.26 0.28 0.3 0.32 0

NDVI_MEAN (V2-V3)

20

30

40

50

60

70

80

90

STD

CN

T

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8

NDVI_MEAN (V8-V9)

20

30

40

50

60

70

80

90

STD

CN

T

0.835 0.84 0.845 0.85 0.855 0.86 0.865 0.87 0.875 0

NDVI_MEAN (R1-R2)

R2=.82 Y= -60.09762 + 439.91944*X P<.0001

R2=.73 Y= -38.03341 + 151.97449*X P<.0001

R2=.09 Y= -422.4636 + 565.87162*X P<.0289


Yield – NDVI Regressions

55

60

65

70

75

80

85

90

YIE

LD

(bu/

a 60

#)

0.2 0.22 0.24 0.26 0.28 0.3 0.32 0

NDVI_MEAN (V2-V3)

55

60

65

70

75

80

85

90

YIE

LD

(bu/

a 60

#)

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8

NDVI_MEAN (V8-V9)

55

60

65

70

75

80

85

90

YIE

LD

(bu/

a 60

#)

0.835 0.84 0.845 0.85 0.855 0.86 0.865 0.87 0.875 0

NDVI_MEAN (R1-R2)

R2=.01 Y= 62.89324 + 14.848695*X P<.5861

R2=.002 Y= 64.773848 + 3.4542164*X P<.73

R2=.04 Y= -54.85259 + 141.58676*X P<.169


Traditional Measurements – NDVI by Treatment


Current Measurements NDVI Measurements

Year Location-

Experiment Variety Treatment SDVIG STDCNT Yield NDVI Mean

(V2-V3) NDVI Mean

(V4) NDVI Mean

(V8-V9) NDVI Mean

(R1-R2)

2011 Johnston-1 93Y13 1 8.2a 72.3a 66.1ab 0.2962ab - 0.7166a 0.8658a

2 8.2a 68.0a 67.1ab 0.2999ab - 0.7131a 0.8645a

3 8.0a 69.5a 72.6a 0.3057a - 0.7257a 0.8632a

4 7.0a 65.3a 69.3ab 0.2868ab - 0.6546ab 0.8223a

5 6.8a 66.3a 64.8ab 0.2777ab - 0.6505ab 0.8567a

6 8.2a 71.8a 69.8ab 0.2949ab - 0.7114a 0.8635a

7 7.0a 64.8a 65.4ab 0.2791ab - 0.6598ab 0.8603a

8 5.8a 57.2a 67.5b 0.2689b - 0.6059ab 0.8442a

9 3.5b 38.2b 62.1c 0.2283c - 0.5577b 0.8502a

Summary • Wide range of relationships between current methods of

evaluating seed treatment and NDVI measurements • The sensors overall did not provide a consistent enough

response to replace the currently used measurements. • Manipulation of the crop sensor use may improve

accuracy • Additional testing of the sensor needed

• Plant height • Leaf area • Fresh weight

Acknowledgements • Thank you!!!!!!

• DuPont Pioneer for providing the opportunity and the sensors used in this project • The DuPont Pioneer research team that planted and maintained the

plots used for this project • Dr. Steve Schnebly – for encouraging me to start the program • Dr. Keith O’Bryan - for encouraging me to continue and finish

• ISU • Dr. Allen Knapp – for working with me on this project and being patient

with my limited writing capabilities • Dr. Tom Loynachan – for being on my committee • Dr. Kenneth Moore – for also being on my committee

• My Family – Kari, Sam, Anna, and Will for putting up with my 7 years of working on this dream of mine

References Bastidas, A. M., Setiyono, T.D., Dobermann, A., Cassman, K. G., Ellmore, R. W., Graef, G. L., Specht, J. E. 2008. Soybean Sowing Date: The Vegetative, Reproductive, and Agronomic Impacts. Crop Sci. 48:727-740 (2008). Blackburn, G. A. 1998a. Spectral Indices for Estimating Photosynthetic Pigment Concentrations: a test using senescent tree leaves, International Journal of Remote Sensing., 19 (4): 657-675. Carter, G.A., Knapp, A.K. 2001. Leaf Optical Properties in Higher Plants: Linking Spectral Characteristics to Stress and Chlorophyll Concentration. American Journal of Botany 88: 677-684. DuPont Pioneer. 2011. Seedling Vigor Score Data Collection Guidelines. Elvidge, C. D. and Chen, Z. 1995. Comparison of Broadband and Narrow-band Red and Near-infrared Vegetation Indices. Remote Sens. Environ. 54: 38-48. Esker, P. D., and Conley, S. P., 2012. Probability of Yield Response and Breaking Even for Soybean Seed Treatments. Crop Sci. 52:351-359. Doi:10.2135/cropsci2011.06.0311 FIS. 1999, Prepared by the Seed Treatment and Envrionment Committee of the International Seed Trade Federation (FIS). 1999. Seed Treatment a Tool for Sustainable Agriculture. Holland Scientific, 2010. Crop Circle ACS-430 User’s Guide. JMP, 11.0. 2013. SAS Institute Inc., Cary, NC Kucharik, C.J. 2006. A multidecadal trend of earlier corn planting in the central USA. Agron. J. 98:1544–1550. Kucharik, C.J. 2008. Contribution of Planting Date Trends to Increased Maize Yields in the Central United States. Agron. J. 100: 328-336.

References (cont.) Liu, W., Tollenaar, M., Stewart, G., and Deen, W. 2004. Response of Corn Grain Yield to Spatial and Temporal Variability in Emergence. Crop Sci. 44:847-854. Munkvold, G. P. 2009. Seed Pathology Progress in Academia and Industry. Annu. Rev. Phytopahtol. 47: 285-311. Nellis, M. Duane; Price, Kevin P.; and Rundquist, Donald, 2009. Remote Sensing of Cropland Agriculture Papers in Natural Resources. Paper 217. Ray, S. S., Singh, J. P., Panigrahy, S. 2010. Use of Hyperspectral Remote Sensing Data for Crop Stress Detection: Ground-based Studies. Remote Sensing and Spatial Information Science. 38:562-567. Rowntree, S. C., Suhre, J. J., Weidenbenner, N. H., Wilson, E. W., Davis, V. M., Naeve, S. L., Casteel, S. N., Diers, B. W., Esker, P. D., Specht, J. E., Conley, S. P. 2013. Genetic Gain × Management Interactions in Soybean: I. Planting Date. Crop Sci. 53: 1128–1138. Russell, P. E., (2005). A Century of Fungicide Evolution. The Journal of Agricultural Science, 143, pp 11-25. doi:10.1017/S0021859605004971. SAS Institute. 2012, SAS Enterprise Guide. Version 5.1. SAS Inst., Cary, NC. Thenkabail, P. S., Smith, R. B., and De Pauw, E. 1999. Hyperspectral Vegetation Indices for Determining Agricultural Crop Characteristics. CEO Research Publication Series q. ISBN: 0-9671303-0-1, Center for Earth Observation, Yale University, USA. 40 Thenkabail, P. S., Smith, R. B., and De Pauw, E. 2000. Hyperspectral Vegetation Indices for Determining Agricultural Crop Characteristics. Remote Sensing Environ. 71(2): 158‐182. Thenkabail, P. S. Enclona, E. A., Ashton, M. S., and Meer, B. Van Der. 2004. Accuracy Assessments of Hyperspectral Waveband Performance for Vegetation Analysis Applications. Remote Sens. Environ. 91: 354-376. Transparency Market Research. 2013. Global Seed Treatment Market – Industry Analysis, Size, Share, Growth,Trends and Forecast, 2012 – 2018. Market Research Report. USDA-NCSS SSURGO and STATSGO Soil Survey Products, 2008 via Google Earth. 2013 Available at http://www.gelib.com/soilweb.htm. William W. Casady, W. W., Palm, H. 2002. Precision Agriculture: Remote Sensing and Ground Truthing. Environmental Quality MU Guide. MU Extension. University of Missouri-Columbia.

http://www.gelib.com/soilweb.htm

Appendix

Seedling Vigor – NDVI Regressions

3

4

5

6

7

8

9

10

SDV

IG

0.22 0.24 0.26 0.28 0.3 0.32 0.34 0

NDVI_MEAN (V2-V3)

R2=.61 Y= -4.20116 + 39.689656*X P<.0001

3

4

5

6

7

8

9

10

SDV

IG

0.55 0.6 0.65 0.7 0.75 0.8 0

NDVI_MEAN (V8-V9)

R2=.01 Y= 23.038767 - 17.824505*X P<.4928

3

4

5

6

7

8

9

10

SDV

IG

0.84 0.845 0.85 0.855 0.86 0.865 0.87 0.875 0

NDVI_MEAN (R1-R2)

R2=.01 Y= 23.038767 - 17.824505*X P<.4928

R2=..40 Y=-3.766576+15.61298*X P<.0001

Stand Count – NDVI Regressions

40

50

60

70

80

90

STD

CN

T

0.22 0.24 0.26 0.28 0.3 0.32 0.34 0

NDVI_MEAN (V2-V3)

R2=.61 Y= -23.95981 + 304.11552*X P<.0001

40

50

60

70

80

90

STD

CN

T

0.55 0.6 0.65 0.7 0.75 0.8 0

NDVI_MEAN (V8-V9)

R2=.44 Y= -25.11303 + 125.73421*X P<.0001

40

50

60

70

80

90

STD

CN

T

0.84 0.845 0.85 0.855 0.86 0.865 0.87 0.875 0

NDVI_MEAN (R1-R2)

R2=.09 Y= 427.7259 - 418.98356*X P<.0323

Yield – NDVI Regressions

50

55

60

65

70

75

80

YIE

LD

(bu/

a 60

#)

0.22 0.24 0.26 0.28 0.3 0.32 0.34 0

NDVI_MEAN (V2-V3)

R2=.00 Y= 64.246396 + 8.9888557*X P<.7685

50

55

60

65

70

75

80

YIE

LD

(bu/

a 60

#)

0.55 0.6 0.65 0.7 0.75 0.8 0

NDVI_MEAN (V8-V9)

R2=.02 Y= 57.200233 + 13.260991*X P<.3703

50

55

60

65

70

75

80

YIE

LD

(bu/

a 60

#)

0.84 0.845 0.85 0.855 0.86 0.865 0.87 0.875 0

NDVI_MEAN (R1-R2)

R2=.10 Y= -156.4162 + 259.61805*X P<.0177

Crop Sensors

• Picture of crop sensors

Crop Sensor Use

Holland Scientific 430 sensor used for measurements in 2011

Documents

Using Crop Sensor Technology to Replace Conventional … · 2017. 4. 13. · • Increased yields realized from planting earlier • Earlier planted soybean lead to increase yields