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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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  

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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!