applications of precision agriculture and remote sensingucanr.edu/sites/paica/files/34496.pdf ·...
TRANSCRIPT
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Applications of Precision Agriculture and Remote Sensing
Richard Plant
Depts. of Biological and Agricultural Engineering and Plant Sciences
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Thanks to
Randy D. Horney
Alvaro Roel
Jorge Perez
Julie Young
Dan Munk
Bruce Roberts
Brock Taylor
Stu Pettygrove
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Site-Specific Management(SSM) and Precision Agriculture
SSM is the management of the crop at a spatial scale less than that of the entire field
PA is the use of information technology to achieve SSM
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Premises on which adoption of SSM is based
Significant within-field variability exists in factors that influence crop yield.
The causes of this variability can be identified and measured.
Information from these measurements can be used to effectively modify crop management practices.
The improvement in economic yield justifies the cost.
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Dealing with Salt Affected Soil
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Sources of variability in irrigated production systems
Soil texture
Salinity
Soil depth
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Factors influencing soil salinity in the western San Joaquin Valley
Re-use of irrigation water from other fields
Pre-existing fossil salts from seabed deposits
Elevated water table
Seepage from drainage canals
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Classification of salt-affected soils
Saline: EC > 4 dS/m, ESP < 15 Can be ameliorated by leaching
Saline-sodic: EC > 4 dS/m, ESP > 15 Leaching alone converts to sodic
Requires amendments in addition
Sodic: EC < 4 dS/M, ESP > 15 Prone to breakdown in structure
Poor infiltration properties
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Most common soil amendments
Gypsum
Sulfur
Sulfuric acid
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Reclamation of saline-sodic soils is well suited to site-specific
management Amendments are relatively expensive
Salt-affected areas tend to be distributed in patches within a field
Calculation of amount of amendment to apply is fairly exact
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Linking EM38 and NDVI measurements to detect and
quantify stress
http://www.ar.nrcs.usda.gov/soils/images/em_38.jpg
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Lillesand, T.M. and Kiefer, R.W. (1994)Remote Sensing and Image Interpretation
NIR -> RR -> GG -> B
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Normalized Difference Vegetation Index
NDVI = (IR-R)/(IR+R)
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Examples of NDVI
R: 175 (IR)
G: 20 (R)
B: 20 (G)
R: 25 (IR)
G: 40 (R)
B: 140 (G)
NIR -> RR -> GG -> B
NDVI = = -0.23
NDVI = = 0.79
25 - 40
25 + 40
175 - 20
175 + 20
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Linking Soil ECa, NDVI, and Yield
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Soil Quality
Vegetation
Yield
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Close-up
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NDVI can be used to detect other types of stress
Water stress trials at WSREC
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Fresno County Site 9-15-97
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NDVI on East (Sandy) and West (Loamy) Sides
NDVI Vs Date - F resno County 1997
0
0.1
0.2
0.3
0.4
0.5
0.6
6/19/9
7
7/3/9
7
7/17/9
7
7/31/9
7
8/14/9
7
8/28/9
7
9/11/9
7
9/25/9
7
D ate
ND
VI
8/11 W
8/25 W
9/5 W
8/11 E
8/25 E
9/5 E
L ast Irrig a tio n
D a tew est (lo am y) a rea
eas t (san d y) a rea
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Remote Sensing of Nitrogen Stress
N D V I vs D a te , W S R E C N T ria l, 1998
0
0 .2
0 .4
0 .6
0 .8
1
1 .2
6/3
0/9
8
7/1
4/9
8
7/2
8/9
8
8/1
1/9
8
8/2
5/9
8
9/8
/98
9/2
2/9
8
10/6
/98
10/2
0/9
8
D a te
ND
VI
5 5 k g /h a
1 1 0 k g /h a
1 6 5 k g /h a
2 2 0 k g /h a
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Example of the use: variable rate N application
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Improving profit with VRT
Increase incomeApply N where needed to increase yields
Cut costsDon’t apply N where it’s not needed
VRT N is profitable when the sum of increased income and savings is greater than the cost of the VRT program.
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Steps of site-specific N management program
On a location by location basis:
Estimate yield potential
Estimate available soil N
Use variable rate technology to adjust the N application rate
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Directed soil sampling
Based on previous year’s yield map, divide the field into 3 zones (high, medium, low yield).
Take soil samples at 3 widely scattered locations in each zone (9 total).
Test sample for residual N (and Ec if salinity is suspected).
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Example: 2002 trial based on 2001 yield map
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Soil N
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All major components
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Management zones
CA&ES Center for Agricultural and Environmental Informatics
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Resulting application map
- 1 1 9 . 9 5 7 7 3- 1 1 9 . 9 5 7 0 6- 1 1 9 . 9 5 7 3 1- 1 1 9 . 9 5 7 2 4- 1 1 9 . 9 5 7 3 6- 1 1 9 . 9 5 6 9 0- 1 1 9 . 9 5 6 7 1- 1 1 9 . 9 5 7 1 2- 1 1 9 . 9 5 8 2 03 6 . 2 1 6 6 43 6 . 2 1 6 1 83 6 . 2 1 5 4 63 6 . 2 1 4 6 83 6 . 2 1 4 0 73 6 . 2 1 3 3 93 6 . 2 1 2 7 93 6 . 2 1 2 5 53 6 . 2 1 1 9 01 M2 H3 L4 L5 M6 L7 H8 M
SH
EE
LY
6-4
VR
T Z
ON
ES
(2
00
2)
SH
EE
LY
6-4
VR
T Z
ON
ES
(2
00
2)
Medium N Zone EC 2.8 to 5.0 > lower yield potential Mean residual N > 50 lb/a Apply: 140 - 50 = 90 lb/a
High N Zone EC 1.4 to 3.5 > higher yield potential Mean residual N > 44 lb/a Apply: 164 - 44 = 110 lb/a
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Yields from 2002 Sheely Trial
0
250
500
750
1,000
1,250
1,500
1,750
2,000
2,250
55 103 148 VRT
N applied (lbs/acre)
Lin
t Y
ield
(lb
s/acre
)
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Economic analysis
Overall question: Does variable rate N application reduce costs sufficiently to overcome the increased cost of implementing the VRT program?
Partial budgets computed for each of the test sites
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VRT Cost per Acre
0
2
4
6
8
10
12
14
16
18
20
250 500 750 1000
Acres under VRT
$ p
er
Acre
Equipment Cost
Equipment, sampling & mapping
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VRT Breakeven profit increase ($/A)
$0.00
$5.00
$10.00
$15.00
$20.00
250 500 750 1000
Acres under VRT
Equipment, sampling & mapping
Breakeven profit increase at 500 acres
$10.24
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Conclusions
Substantial savings in fertilizer even with conservative VRT N management
For all 3 sites profit increased primarily from decrease in N use and not from increasing N use
Breakeven fertilizer savings decreases with acreage increase
Breakeven acreage decreases with increases in fertilizer, labor, or fuel costs
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Conclusions, contd.
Profit margin can be increased by using the equipment for other purposes or for other crops.
Some recommended potential uses:
Variable rate soil amendment application.
Variable rate Pix on cotton
Variable rate seeding