Post on 18-Dec-2015
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)
5
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
6
7
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
8
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
9
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
rea
din
g
A-SCN A-SDS A-SS A-UN
10
Soybean diseases: SCN & SDSSDS susceptible SDS & SCN susceptible
SDS & SCN Resistant SCN Susceptible
Found differences in chlorophyll content between infested and healthy plants
11
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
12
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
32
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