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Characterizing Variability In Soil Test Values In Northeast Iowa
Patrick Reeg Crea-ve Component
Agenda • My Background
– Experiences on the family farm – Family – Educa-on – Work experience – Ac-vi-es
• Why I pursued a Master of Science in Agronomy
• Crea-ve component defense
Family Farm
Family Farm
Grandparents Farm
Family Farm
• 226 Total Acres in Jackson County – 113 acres pasture – 113 acres -llable – 23 different fields on the farm – Average field size is 4.9 acres
• Crops include alfalfa, corn, oats, soybeans • Livestock include hogs and caSle
August 1993 -‐ May 1995 August 1991 – May 1993
May -‐ December 1995
December 1995 -‐ August 2005 September 2005 -‐ Present
Educa>on & Work Experience
N
EW
S
2007 On Farm Network Projects # Guided Stalk Sampling# Replicated Strip Trials# Scouting Network# Watershed Programmingwww.iasoybeans.com
www.isafarmnet.com
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## $
$ $ $ $$$
$$ $$ $
$$ $$$$ $ $
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$
LINN
LEE
SAC
TAMA
IDA
SIOUX
POLK
CLAY
LYON
IOWA
CASS
KOSSUTH
PAGE
JASPER
ADAIR
JONESBENTON
CLAYTON
DAVIS
STORY
CEDAR
FAYETTE
CLINTON
BOONE
PLYMOUTH
MONONA
DALLAS
MILLS
FLOYD
SHELBY
OBRIEN
HARDIN
BUTLER
WOODBURY WEBSTER
MARION
WAYNE
WRIGHT
KEOKUK
SCOTT
JACKSON
GREENE
TAYLOR
HARRISON
UNION
GUTHRIE
LUCAS
JOHNSON
WARREN
HENRY
DUBUQUE
CARROLL
MADISON
CRAWFORD
ADAMS
FRANKLIN
MAHASKA
CALHOUN GRUNDY
HANCOCK
LOUISA
EMMET
HAMILTON
ALLAMAKEE
POTTAWATTAMIE
DECATUR
CLARKE
WINNESHIEK
MARSHALL
FREMONT
WORTH
BREMERCHEROKEE
HOWARD
PALO ALTO
DELAWAREBUCHANAN
RINGGOLD
MONROE
POWESHIEK
MITCHELL
WAPELLO
AUDUBON
BUENA VISTA
BLACK HAWK
VAN BUREN
OSCEOLA
POCAHONTAS
CHICKASAW
APPANOOSE
WASHINGTON
HUMBOLDT
MUSCATINE
CERRO GORDO
JEFFERSON
DICKINSON
DES MOINES
WINNEBAGO
MONTGOMERYST 20 07 22 9B
ST 20 07 24 0A
Ac>vi>es • Cer-fied Crop Advisor, Iowa CCA Board Director
• Hawkeye Community College –Ag Business Advisory CommiSee Member
• 1st & 2nd place winner of the Visual Image Contest at the 6th Interna-onal Conference on Precision Agriculture
My MS in Agronomy Timeline • 2001 I learned about the program • 2002 I provided a tour at AgVantage FS to the ISU freshman agronomy class with Tom Loynachan & Mary Wiedenhoeb
• April 10, 2003 turned down because my GPA did not meet the program’s minimum requirements
• August 19, 2003 accepted to the MS in Agronomy distance educa-on program.
Why obtain an MS in Agronomy?
• Advance my educa-on • Open new opportuni-es • Increase the value I could provide growers
Acknowledgments • Family • AgVantage FS, Inc • Iowa Soybean Associa-on • Proctors
– Connie Sharff – Lisa Renze
• ISU Faculty & Staff – Tom Loynachan – Mary Wiedenhoeb – Jesse Drew
Acknowledgments
Program of Study CommiIee • Richard Cruse, Major Professor • Ken Moore, CommiSee Member • Lee Burras, CommiSee Member
Characterizing Variability In Soil Test Values In Northeast Iowa
Patrick Reeg Crea-ve Component
Objec>ve • Use exis-ng grid sampling data to characterize variability in soil test values in northeast Iowa – Field – County – Landform region – Predominate soils – Impact of variable rate over -me
Fer>lizer Prices In Dollars per Ton
$170 $164 $165$181
$245$273
$244$227
$250$276
$303
$337
$0
$50
$100
$150
$200
$250
$300
$350
$400
2001 2002 2003 2004 2005 2006
Muriate of Potash 60-‐62% K2O 18-‐46-‐ 0 (DAP)
Increasing Fer>lizer Costs
• Fer-lizer demand • Ethanol boom • Weaker US dollar • Higher produc-on & transporta-on costs
Increasing Fer>lizer Costs
$300
$425
$580
$420
$500
$680
$200
$300
$400
$500
$600
$700
$800
August 2007 December 2007 April 2008
Fertilizer P
rice/Ton
0-‐0-‐60 (Potash) 11-‐44-‐0 (Map)
Soil Test Variability -‐ Natural Factors • Topography • Soil texture • Drainage • Slope • Erosion poten-al
Soil Test Variability -‐ Man Made Factors • Commercial fer-lizer applica-ons/misapplica-ons
• Manure • Cropping rota-ons • Yield • Crop residue distribu-on and removal • Tillage • Ar-ficial Drainage • Land Shaping • Loca-on of old livestock lots • Fence rows • Historic field boundaries
Soil Test Variability
Sampling Limita>ons
• Normalized Difference Vegeta-on Index image of a field’s corn canopy collected July 21, 2006
• Caused by misapplica-on of poultry manure
• Represen-ng the variability found on this scale would be impossible with tradi-onal soil sampling alone
Most Vegetative 100
0
Least Vegetative
reduced manure rate
Materials and Methods
• 3,481 fields sampled from 1994 to 2005 • Majority of fields sampled on 2.5 acre grids • Soil tests included:
– Bray P1 for phosphorus – ammonium acetate for potassium – 1:1 water to extract pH – SMP was used to analyze buffer pH
• Data stored spa-ally in Arcview GIS
#######
# # # # # # # #
##########
# # # # # # # # # #
##########
# # # # # # # # # #
##########
# # # # # # # # # #
########
# # # # # # #
########
# # # # # # # #
1234567
8 9 10 11 12 13 14 15
16171819202122232425
26 27 28 29 30 31 32 33 34 35
36373839404142434445
46 47 48 49 50 51 52 53 54 55
56575859606162636465
66 67 68 69 70 71 72 73 74 75
7677787980818283
84 85 86 87 88 89 90
9192939495969798
99 100 101 102 103 104 105 106
Grid soil sampled field in FayeIe County
Summary of Grid Soil Samples By County County Number of fields Average field size Total area Number of samples
Allamakee 28 56 1,555 1,333Black Hawk 163 68 11,015 6,575Bremer 217 74 16,151 9,679Buchanan 364 88 32,067 21,094Butler 113 71 8,066 4,616Cerro Gordo 157 103 16,106 8,045Chickasaw 251 78 19,561 12,890Clayton 56 62 3,451 1,733Delaware 34 97 3,287 1,200Fayette 688 82 56,736 39,972Floyd 244 94 22,871 11,192Franklin 194 97 18,894 8,279Grundy 129 98 12,591 7,299Hamilton 62 102 6,328 3,021Hancock 85 110 9,335 3,729Hardin 304 95 28,998 13,280Howard 169 88 14,930 8,248Mitchell 59 110 6,477 2,869Winneshiek 58 66 3,856 1,879Wright 63 121 7,650 3,455Multiple Counties¹ 43 124 5,319 2,298
County Summary 3,481 88 305,245 172,686¹ Fields spatially arranged in multiple counties
---------- acres ----------
Results and Discussion
• 3,481 Fields • Average field size is 88 acres • 305,245 total acres • 172686 total soil samples • 115,094 first -me samples
First Time Samples (115,094 samples)
• Iowan surface 81,150 samples • Des Moines Lobe 24,821 samples • Paleozoic Plateau 6,746 samples • Southern Drib Plane 2,377 samples (not used)
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#
Linn
Sac
Lee
Ida
Sioux
Polk
Clay
Tama
Iowa
Lyon
Kossuth
Cass
Story
Adair
Jasper
Clayton
Page
Clinton
Benton
Butler
Fayette
Mills
Jones
Cedar
Dallas
Plymouth
Floyd
Scott
Davis
Boone
Wright
Monona
Shelby
Hardin
Taylor
Carroll
Woodbury
Obrien
Webster
Marion
Harrison Guthrie
Jackson
Crawford
Keokuk
Greene
Warren
Wayne
Johnson
HenryUnion
Franklin
Lucas
Calhoun Grundy
Dubuque
Decatur
Pottawattamie
Marshall
Madison
Clarke
Worth
Hancock
Hamilton
Palo Alto
Louisa
Mahaska
Allamakee
Fremont
Mitchell Howard
Delaware
Ringgold
Adams
BremerCherokee
Winneshiek
Monroe
Buchanan
Emmet
Poweshiek
Wapello
Audubon
Pocahontas
Black Hawk
Osceola
Buena Vista
Washington
Jefferson
Chickasaw
Van Buren
Cerro Gordo
Appanoose
Humboldt
Muscatine
Dickinson
Des Moines
Winnebago
Montgomery
Des Moines Lobe
Iowan Surface
#
Paleozoic Plateau
Southern Iowa Drift Plain
Grid Soil Samples By Landform Region
0%
5%
10%
15%
20%
25%
30%
35%
<= 5.4 5.5 - 5.9 6.0 - 6.4 6.5 - 6.9 >= 7.0
Perc
ent o
f sam
ples
Des Moines LobeIowan SurfacePaleozoic PlateauAll first time grid samples
pH Sample Distribu>on By Landform Regions
Phosphorus Sample Distribu>on By Landform Regions
0%
10%
20%
30%
40%
50%
60%
0-8 9-15 16-20 21-30 31+
Very Low Low Optimum High Very High
Perc
ent o
f sam
ples
Des Moines LobeIowan SurfacePaleozoic PlateauAll first time grid samples
Potassium Sample Distribu>on By Landform Regions
0%
5%
10%
15%
20%
25%
30%
35%
0-90 91-130 131-170 171-200 201+
Very Low Low Optimum High Very High
Perc
ent o
f sam
ples
Des Moines LobeIowan SurfacePaleozoic PlateauAll first time grid samples
Soil Test Summary by Soil Series Soil Series CSR Total area Number of samples pH average P average K average
AcresBassett 67 7,838 2,977 6.4 29 156Canisteo 79 6,118 2,117 7.1 33 185Clarion 70 18,684 6,849 6.1 35 168Clyde 74 23,909 9,228 6.6 33 153Clyde-Floyd Complex 74 17,715 6,455 6.5 36 160Downs 72 6,483 2,531 6.5 47 196Fayette 52 5,165 2,212 6.6 41 143Floyd 78 17,436 6,598 6.5 33 161Harps 63 5,225 1,799 7.5 26 173Kenyon 64 34,202 12,527 6.4 33 168Maxfield 89 5,067 1,875 6.5 28 165Nicollet 90 7,005 2,580 6.3 37 188Oran 82 6,229 2,376 6.4 29 153Ostrander 80 4,694 1,803 6.3 35 165Readlyn 87 14,626 5,504 6.4 32 166Saude 53 5,688 2,265 6.3 42 164Tama 84 7,518 2,902 6.2 36 194Tripoli 80 7,224 2,762 6.6 31 152Webster 84 6,470 2,465 6.6 34 183Webster-Nicollet Comp 86 6,550 2,330 6.4 37 171
Soil Series Summary 74 213,845 80,155 6.4 34 166
------------ ppm ------------
Soil Test Summary by Soil Series
• Summarized the top 20 soils bases on acres • Represented 213,845 acres • Average CSR was 74 • 80,155 samples • pH Average 6.4 • P Average 34 • K Average 166
Number of Samples by pH Category Table 3. Number of samples by pH category
Soil Series <= 5.4 5.5 - 5.9 6.0 - 6.4 6.5 - 6.9 >= 7.0 TotalBassett 115 606 884 845 527 2977Canisteo 80 183 308 282 1264 2117Clarion 1025 2118 1799 1009 898 6849Clyde 217 1132 2494 3114 2271 9228Clyde-Floyd Complex 216 1008 1967 1974 1290 6455Downs 86 326 740 810 569 2531Fayette 77 277 516 668 674 2212Floyd 200 1128 1939 1987 1344 6598Harps 31 74 117 136 1441 1799Kenyon 468 2447 3982 3642 1988 12527Maxfield 52 311 584 480 448 1875Nicollet 224 730 736 402 488 2580Oran 105 422 693 680 476 2376Ostrander 88 417 550 508 240 1803Readlyn 153 1000 1780 1652 919 5504Saude 156 551 782 483 293 2265Tama 171 701 1088 672 270 2902Tripoli 26 276 801 962 697 2762Webster 129 493 582 490 771 2465Webster-Nicollet Comp 221 510 598 373 628 2330Soil Series Summary 3840 14710 22940 21169 17496 80155
pH Sample Distribu>on By Soil Series
0%10%20%30%40%50%60%70%80%90%
100%
Perc
ent o
f Sam
ples
<= 6.5>= 6.5
P Sample Distribu>on By Soil Series
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Perc
ent o
f Sam
ples
<= 1516+
K Sample Distribu>on By Soil Series
0%
10%
20%
30%
40%
50%
60%
70%
80%
Perc
ent o
f Sam
ples
<= 130131+
Percent of Fields By Soil Test Category
5
19
2925
22
6
18
13
23
40
15
2422
12
26
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Very Low Low Optimum High Very High
Perc
ent o
f Fie
lds
pHPK
Phosphorus To Potassium Correla>on Based On Field Averages
y = 1.758x + 103.2R² = 0.400
0
100
200
300
400
500
600
700
0 50 100 150 200 250 300 350
K p
pm
P ppm
pH Change Over Time
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
5.00
5.50
6.00
6.50
7.00
7.50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Lim
e R
ate
(Ton
s/Acr
e)
pH
Sample ID
Figure 15. Grundy County Field - pH By Year After Lime Application
pH 2000 pH 2001 pH 2002 pH 2003 Lime Rate (Tons/Acre)
5.0
5.5
6.0
6.5
7.0
7.5
<= 5.4 5.5 - 5.9 6.0 - 6.4 6.5 - 6.9 >= 7.0
pH
pH Category
pH First TimepH Second Time
pH Retest Comparison
426 1966 3102 2974 2101
5
15
25
35
45
55
65
0-8 9-15 16-20 21-30 31+
P pp
m
Phosphorus Category
P First TimeP Second Time
Phosphorus Retest Comparison
426 1966 3102 2974 2101
679 2088 1479 2594 3729
5
55
105
155
205
255
305
0-90 91-130 131-170 171-200 201+
K p
pm
Potassium Category
K First TimeK Second Time
Potassium Retest Comparison
426 1966 3102 2974 2101
2978 2932 1983 904 1772
Conclusion
• Soil tes-ng is a valuable tool • Soil tes-ng has limita-ons • Iden-fying soil test trends and rela-onships can help agronomists fine tune fer-lity recommenda-ons and improve grower profitability.
• Collec-ng this data spa-ally using GPS and GIS makes this type of analysis possible
Prac>cal Use Field Acres 0-‐8 9-‐15 16-‐20 21-‐30 31+ 0-‐15 PPM Total Samples 0-‐15 PPM Total Acres RankF 45.0 50.0% 33.3% 11.1% 0.0% 5.6% 83.3% 37.5 1Q 55.0 18.2% 54.5% 4.5% 13.6% 9.1% 72.7% 40.0 2D 57.5 30.4% 30.4% 8.7% 17.4% 13.0% 60.9% 35.0 3G 57.5 13.0% 34.8% 0.0% 21.7% 30.4% 47.8% 27.5 4C 55.0 18.2% 27.3% 22.7% 4.5% 27.3% 45.5% 25.0 5O 27.5 9.1% 27.3% 0.0% 27.3% 36.4% 36.4% 10.0 6A 57.5 4.3% 21.7% 4.3% 21.7% 47.8% 26.1% 15.0 7K 100.0 2.5% 22.5% 10.0% 30.0% 35.0% 25.0% 25.0 8J 52.5 4.8% 14.3% 23.8% 9.5% 47.6% 19.0% 10.0 9P 55.0 13.6% 9.1% 9.1% 22.7% 45.5% 22.7% 12.5 9E 57.5 0.0% 17.4% 30.4% 17.4% 34.8% 17.4% 10.0 10L 25.0 0.0% 10.0% 20.0% 10.0% 60.0% 10.0% 2.5 11M 75.0 3.3% 0.0% 10.0% 13.3% 73.3% 3.3% 2.5 12B 52.5 0.0% 0.0% 4.8% 38.1% 57.1% 0.0% 0.0 13H 47.5 0.0% 0.0% 5.3% 0.0% 94.7% 0.0% 0.0 13I 52.5 0.0% 0.0% 9.5% 28.6% 61.9% 0.0% 0.0 13N 40.0 0.0% 0.0% 0.0% 6.3% 93.8% 0.0% 0.0 13Total Acres 912.5 252.5
Conclusion
• Incorpora-ng addi-onal precision agriculture tools such as remote sensing and yield monitors can help evaluate and measure the true return on investment of prac-ces such as grid soil sampling and variable rate applica-ons.
Ques>ons
Thank You!