Bajwa NCERA 180 Meeting, 23-25 March 2011, Little Rock, Arkansas 1

Precision Agriculture in Arkansas

Sreekala G. Bajwa Associate Professor, Dept of Biological & Agricultural Engineering,

University of Arkansas Division of Agriculture, Fayetteville

Dharmendra Saraswat, Subodh Kulkarni, Leo Espinoza, Terry Griffin

University of Arkansas -Division of Agriculture, Little Rock

Bajwa 2

At a Glance

• Overview of Arkansas Agriculture• Current and past PA projects• Current & Future Issues and needs

Arkansas Agriculture

Crop Acres Planted (×1000)

Acres Harvested (×1000)

Production (tons) Value of Production (million $$)

Rice 1791 1785 52.5 million 1330

Soybean 3190 3150 3 million 1245

Cotton 545 540 257 K 395.9

Corn 390 380 1.45 million 267.9

Hay 1 1480 2.68 million 200

Wheat 200 150 220 K 42

G.Sorghum 40 35 68.5 K 11.2

Bajwa NCERA-180, 2011 3

Agriculture sector accounts for 12% of Gross State Product

Farm Characteristics (USDA-NASS)Farm Characteristics Year 1997 Year 2007

Average Farm size (acres) 300 281

Farms size 1000 acres, % 6.8 6.5

Farms size 99 acres, % 44.5 54.3

Farm sale < $9,999 57 59.6

Farm sale $250,000 13.7 13.1 (91% sales)

Farm sale $500,000 6.6 9.3 (81.6% sales)

Average operator age 53.4 56.5

Farm land in conservation, acres 188,902 441,655

NCERA180 4

Total land: 33.29 million acresTotal farm land 13.87 million acresTotal Population: 2.9 million

Gandonou et al (2001): 1060 ac to purchase PA equipment1350 ac in AR (Popp & Griffin, 2000)

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Precision Agriculture Adoption• No comprehensive data available on PA

adoption in Arkansas• Arkansas lags behind other regions in PA

adoption• Most popular technologies

– Yield monitoring– Soil grid sampling & zone management– Variable rate application– Remote Sensing– On-the-go sensing

Popp and Griffin (2000); Groves et al. (2006); Torbett et al (2008); Winstead et al (2010)

Summary of Precision Agricultural Projects

in Arkansas

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Precision Agriculture Projects: Remote Sensing

• Optical remote Sensing of plant response to stressors– N stress in rice and cotton– Water stress in cotton– Compaction in cotton fields– Diseases in soybean

• Soybean Cyst Nematode• Sudden Death Syndrome & interaction with water stress• Charcoal rot & interaction with water stress

• For early detection of stresses• For site specific management

Bajwa, Rupe, Kulkarni, Norman, Mozaffari, Vories, Huitink

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Soybean Diseases: SCN & SDS Project Bajwa, Kulkarni, Rupe

• Both SCN and SDS are Soil-borne pathogens, difficult to detect

• SCN is a major cause of yield loss ($1.69 billion in the US in 1998)

• SCN symptoms are similar to water/nutrient stress, and hence difficult to detect

• SCN and SDS interact

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Soybean Diseases: SCN & SDS Project

• To detect and map SCN and SDS incidence• Several experiments – microplot, field strip plot with

cutlivars, field plots with irrigation treatments• Microplot experiment

– 4 cultivars: Control (SCN & SDS resistant), SCN resistant, SDS resistant, SCN & SDS susceptible

– 4 disease treatments: Control, SCN, SDS, SCN & SDS– 2 years, 1 location

ySS = -0.008x2 + 1.70x - 47.485

R2 = 0.30

yUN = -0.003x2 + 0.72x + 0.81

R2 = 0.40

ySDS = -0.005x2 + 1.10x - 18.44

R2 = 0.25

ySCN = -0.002x2 + 0.475x + 12.27

R2 = 0.28

20

25

30

35

40

45

50

70 80 90 100 110 120 130 140

Days after planting

Ch

loro

ph

yll m

eter

rea

din

g

P-SCN P-SDS P-SS P-UN

ySDS = -0.006x2 + 1.26x - 25.78

R2 = 0.52

ySS = -0.008x2 + 1.72x - 49.46

R2 = 0.43

yUN = -0.003x2 + 0.75x - 6.09

R2 = 0.69

ySCN = -0.004x2 + 1.04x - 20.69

R2 = 0.59

20

25

30

35

40

45

50

70 80 90 100 110 120 130 140

Days after planting

Ch

loro

ph

yll m

eter

rea

din

g

H-SCN H-SDS H-SS H-UN

ySS = -0.01x2 + 1.94x - 42.87

R2 = 0.62

ySDS = -0.014x2 + 2.77x - 91.31

R2 = 0.61

yUN = -0.004x2 + 1.00x - 11.64

R2 = 0.42

ySCN = -0.006x2 + 1.25x - 21.34

R2 = 0.28

20

25

30

35

40

45

50

55

70 80 90 100 110 120 130 140

Days after planting C

hlo

rop

hyl

l met

er r

ead

ing

E-SCN E-SDS E-SS E-UN

ySDS = -0.006x2 + 1.26x - 25.78

R2 = 0.52ySS = -0.008x2 + 1.72x - 49.46

R2 = 0.43

yUN = 0.176x + 23.24

R2 = 0.66ySCN= 0.14x + 27.96

R2 = 0.59

20

25

30

35

40

45

50

55

70 80 90 100 110 120 130 140

Days after planting

Ch

loro

ph

yll m

eter

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din

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A-SCN A-SDS A-SS A-UN

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Soybean diseases: SCN & SDSSDS susceptible SDS & SCN susceptible

SDS & SCN Resistant SCN Susceptible

Found differences in chlorophyll content between infested and healthy plants

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Soybean diseases: SCN & SDS• There were

differences in reflectance between infested and non-infested plants over time

Control SCN

SDS SCN_SDS

Correlation with Canopy Reflectance

• Difficulty in getting plants infested

• Some cross-contamination• Lack of good means of

measuring infestation levels– Presence of pathogen does

not mean infestation

• Confounding environment

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Research Problem: • To investigate cultivar, drought effects,

and charcoal rot response on soybean

canopy reflectance (ASD spectro-

radiometer and CropCircleTM ACS-470)

• To develop a method to detect and map

charcoal rot

Soybean Charcoal Rot StudyDoubledee, Rupe, Kulkarni, Bajwa

Background Information: •38M bu. lost/year •Prevalent in heat and drought stressed

areas •Irrigated soybeans exhibit charcoal rot at

critical

plant stages after flowering begins•Disease symptoms depends on plant’s

growth

stage at the time of infestation

Research Experiment: •2 disease treatments (inoculated and not

inoculated), 2 water regimes (irrigated and

water stressed), and 5 replications •4 soybean cultivars DT-97-4290

(moderately

resistant), DP-4546 (moderately resistant),

R-01-581FCR (drought tolerant),

and LS-980358 (susceptible) • Crop CircleTM ACS-470, ASD spectro-

radiometer

Results : •CropCircle: GNDVI, NDVI, VI= f(infestation)•ASD spectra: 12 vegetation indices were

tested

Practical Application: • Sensors detected charcoal rot before

physical symptoms were observed. However, this

was not consistent at all times during the growth season

• Infested plants had higher vegetation indices (CWSI NDVI, REIP, WI, D-Chl-ab, SAVI and SIPI) than non-infested plants at certain times during the season

Intr

odu

ctio

n-p

HVariable Rate Liming

Saraswat, Espinoza, Kulkarni, Griffin

Cos

t of

Lim

e in

AR

$20/ton

$25/ton

$30/ton

$35/ton

$45/ton

$10/ton

Var

iab

le R

ate

Lim

ing

Lime recommendation based on 2.5 ac grid soil

sampling results

Lime recommendation

based on MSP sensor data

Cost of Uniform Liming (recommendation 2 t/ac lime) , @$25/ton = approx. 66*25*2 = $ 3300Cost of variable rate liming, @$25/ton = 1.5 * 8 * 25 = $300Savings = approx. $3000

VR

T C

omp

onen

tsJohn Deere 6230 Tractor

Barron Brothers International

Grasshopper High Clearance Spreader

Two 24 inch spinner disk 21 inch Conveyer Chain

VR

T C

omp

onen

ts

PTO Hydraulic Pump

Spinner Hydraulic Pressure Limit Valve

Conveyer HydraulicPressure Limit Valve

TeeJet ConveyerControl Valve

TeeJet SpinnerControl Valve

Hydraulic Fluid Reservoir

VR

T C

omp

onen

tsDickey John 360 Conveyer Rate Sensor

Attached to post weldedto conveyer shaft

VR

T C

omp

onen

ts

Spinner Shaft RPM Sensor

RPM Sensor PickUp Contact Point

VR

T C

omp

onen

tsConveyer On/Off Switch

Custom Box to Protect Wires

TeeJet Dual Control Module

VR

T C

omp

onen

ts

Gate Height AdjustmentWheel With Lock

Gate Height AdjustableFrom 1 to 12 Inches

Adjustable Drop PointFor Distribution Control

Fie

ld M

eth

odol

ogy

Target rate of 300 lbs over 11 pans across a 40 ft swath to determine swath distribution and applied amount

Pulse rate of 1500 on DJ360 rate controller for 3” gate height at spinner rpm of 500 provided the closest match

Lime density: 83 lbs/cu ft

Travel speed: 6 mph

Fie

ld M

eth

odol

ogy

400 Feet Consistingof Two Rate Zones

15 Feet

15 Feet

21 pans for each rate zonePans within a row were 9.5 ft apart

Pre

lim

inar

y R

esu

lts

• Similar results when transitions from 600 lb to 300 lb, 600 lb to 900 lb, and 900 lb-600 lb were tested

Su

mm

ary

A variable rate spreader system for lime application was put together

Missing parts and faulty part operation caused confusion

Manufacturer suggested procedure was revised to calibrate the spreader

Over application in the lower distribution and under application at higher distribution setting was observed

The spreader is under further evaluation

Current/Future Issues• Water quantity and quality

– Mississippi Alluvial Aquifer Drying at 15 cm/yr – Arkansas 5th in irrigated acreage and second in percentage

of crop area irrigated (Census 2007), with ~ 94% of ground water used for irrigation in Arkansas (USGS, 2005)

– Low aquifer recharge rate of 2 cm/yr

• Climate Change– Climate adaptation and mitigation– Water availability and quality– Pest and disease incidence

• Energy - Fuel prices31

Some of the Current Issues Raised by Growers

• Soil grid sampling – Value of grid sampling? what is the right grid size?

• Pest detection and site-specific management• Data management and information

extraction• Challenges with equipment • Getting the most out of precision agriculture

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Special Thanks to..Cotton IncorporatedCotton Foundation

United Soybean BoardCorn and Grain Sorghum Promotion Board

Deano Traywick, Paul Ballantyne, and M. Ismanov,Dr. John Fulton, Auburn UniversityBrian Mathis, TeeJet Engineer

ACKNOWLEDGEMENT