Goal:
The yield potential of different varieties of the same species varies greatly. Our goal is to, using the information that is readily available to producers and differentiating by specific environments, to lend objective analysis to the process of determining the genetics with the greatest yield potential.

Description of the problem:
One of the most significant factors affecting the profitability of a crop enterprise is the selection of the variety to plant. When growing corn, these yield differences can be as much as 20 or more bushels per acre. Under current economic conditions, this difference of $35/acre (corn at $1.75/bu) of revenue may determine if the field will show profit or loss. Unfortunately, many farmers spend less time or effort determining which varieties they will plant than is economically justified.

Practical problem:
Extensive data bases of variety yield information are compiled by seed companies as they accomplish for advertising and information side by side comparisons. Seed companies frequently encourage their seed dealers to establish at least one local side by side variety trial. Results from these trials are published on the web and in company reports. Further analysis of this data by producers and their consultants can result in improved management. This paper presents one method of analysis of this data. From a professional statistician¡¯s perspective, there are significant theoretical problems associated with how these side by side trials are designed and thus how they can be analyzed. The method described here fails to overcome these problems. However, a question that could be fairly asked is which is the greater error? 1). Our failure to attempt to further glean more knowledge from this trove of information. 2). Our attempt to further analysis data with known significant statistical problems resulting from a less than ideal experimental design. Obviously our conclusion is that we should attempt to further analyze the data despite the data¡¯s statistical problems.

We have looked at the standard deviation across years at single sites from the 30 years of precipitation data used to generate the map below. We have found the standard deviation from the mean to fall between 4.6 and 4.9 inches. This means that if the precipitation data is normally distributed, about 33% of the time we can expect the annual precipitation to be greater than or less than the mean ¡À ¡Ö4.75 inches of precipitation (4.6 to 4.9). We can look at the map below and crudely translate the 4.75 inches to a east ¨C west distance of 100 to 150 miles. This leads us to the conclusion that data taken 100 to 150 miles in both east and west of our location of interest is pertinent data to our decision making process. A north ¨C south distance is more difficult to quantify but given climatic variability, a distance of 50 miles in either north or south direction is justifiable. This defines the block of interest to us for analysis.


In this area of influence we will collect all side by side comparisons with greater than ¡Ö5 or more varieties being compared. We will compile all of this data into a single spread sheet in the following step wise manner

Continue to steps 1 through 4 >

Step 1.
From the discussion above, side by side comparisons that are within 100-150 miles of radius from our farming area will be considered. Compile as much of this data as is available and load the data into a spreadsheet as follows.

Company

Variety

yield

Farmer

pioneer

37m81

176.4

Dedrich,D

pioneer

3751

174.2

Dedrich,D

pioneer

37h24

173.4

Dedrich,D

Step 2.
Number each row.
Insert column A. In A1 put 1. In A2 put = A1+1. Fill down to bottom of yield data. Select column A. Copy. Paste Special, values. (these last three key stokes appear to do nothing but are critical to this analysis. They remove the equations. Not doing this will create a mess later on)

A

B

C

D

E

1

Company

Variety

Yield

Farmer

2

pioneer

37m81

176.4

Dedrich,D

3

pioneer

3751

174.2

Dedrich,D

4

pioneer

37h24

173.4

Dedrich,D

Step 3.
Calculate an index for each variety within each farmer test site (hereafter all plots at one location will be referred to as a site).
In column F in the last row of each farmer’s site, calculate the average of each site. Select column F. Copy. Paste special, values. Copy up for each farmer’s site to fill all of column F.

In column G for each variety within each plot calculate an index as follows.

In cell G2, Index = (yield-farmer’s plot average)/ farmer’s plot average. Select the column from G2 to the bottom of the spreadsheet and copy down.

Select column G. Copy. Paste Special, values.

A

B

C

D

E

F

G

1

Company

Variety

yield

Farmer

average

Index

2

pioneer

37m81

176.4

Dedrich,D

169.7

0.039481

3

pioneer

3751

174.2

Dedrich,D

169.7

0.026517

4

pioneer

37h24

173.4

Dedrich,D

169.7

0.021803

Step 4.
Select entire spreadsheet. Sort by Company, Variety.

A

B

C

D

E

F

G

1

Company

Variety

yield

Farmer

average

Index

3

pioneer

3751

174.2

Dedrich,D

169.7

0.026517

27

pioneer

3751

142.7

kasperson

154.7659

-0.07796

56

pioneer

3751

154.7

sanderson

163.175

-0.05194

67

pioneer

3751

157.7

Vanderwal

177.5429

-0.11176

84

pioneer

3751

152.3

volkers

147.1067

0.035303

90

pioneer

3751

160

hecla

157.61

0.015164

99

pioneer

3751

139.9

harry

150.4143

-0.0699

Continue to steps 5 through 8 >

Step 5.
Calculate for each variety it’s index average.
In Column H, at the bottom of each variety, calculate the variety index average for each variety. Select column H. Copy. Paste special, values. Select column H, copy, and paste into column I. Fill I up within each variety, so there is an index average in every row.

A

B

C

D

E

F

G

H

I

1

Company

Variety

yield

Farmer

average

Index

var ave ind

var ave ind

3

pioneer

3751

174.2

Dedrich,D

169.7

0.026517

-0.03351

27

pioneer

3751

142.7

kasperson

154.7659

-0.07796

-0.03351

56

pioneer

3751

154.7

sanderson

163.175

-0.05194

-0.03351

67

pioneer

3751

157.7

Vanderwal

177.5429

-0.11176

-0.03351

84

pioneer

3751

152.3

volkers

147.1067

0.035303

-0.03351

90

pioneer

3751

160

hecla

157.61

0.015164

-0.03351

99

pioneer

3751

139.9

harry

150.4143

-0.0699

-0.03351

Step 6.
Calculate for each variety it’s index standard deviation.
In Column J, at the bottom of each variety, calculate the variety index standard deviation. Select column J. Copy. Paste special, values. The standard deviation will give you an estimate of how consistent a variety is.

<>< </>

A

B

C

D

E

F

G

H

I

J

1

Company

Variety

yield

Farmer

average

index

var av ind

var av ind

std dev

3

pioneer

3751

174.2

Dedrich,D

169.7

0.026517

-0.03351

27

pioneer

3751

142.7

kasperson

154.7659

-0.07796

-0.03351

56

pioneer

3751

154.7

sanderson

163.175

-0.05194

-0.03351

67

pioneer

3751

157.7

Vanderwal

177.5429

-0.11176

-0.03351

84

pioneer

3751

152.3

volkers

147.1067

0.035303

-0.03351

90

pioneer

3751

160

hecla

157.61

0.015164

-0.03351

99

pioneer

3751

139.9

harry

150.4143

-0.0699

-0.03351

-0.03351

0.058411

Step 7.
Select all data. Sort by column H.
This will rank varieties by their relative competitiveness within this years data.

A

B

C

D

E

F

G

H

I

J

1

Company

Variety

yield

Farmer

average

index

var av ind

var av ind

std dev

104

pioneer

37r71

169.4

harry

150.4143

0.126223

0.116369

0.116369

0.066562

74

pioneer

35n05

149.9

volkers

147.1067

0.018988

0.080594

0.080594

0.096225

103

pioneer

36f30

159.7

harry

150.4143

0.061734

0.062866

0.062866

0.08217

102

pioneer

38p06

152.2

harry

150.4143

0.011872

0.029312

0.029312

0.083897

96

pioneer

38w36

150.8

hecla

157.61

-0.04321

0.026706

0.026706

0.054416

100

pioneer

37h24

151.9

harry

150.4143

0.009877

0.014986

0.014986

0.006144

76

pioneer

3559

158.9

volkers

147.1067

0.080169

0.014339

0.014339

0.070776

88

pioneer

3730

161.7

hecla

157.61

0.02595

-0.00433

-0.00433

0.056348

79

pioneer

35r57

147.9

volkers

147.1067

0.005393

-0.01567

-0.01567

0.020111

101

pioneer

38p05

141.1

harry

150.4143

-0.06192

-0.02366

-0.02366

0.037435

99

pioneer

3751

139.9

harry

150.4143

-0.0699

-0.03351

-0.03351

0.058411

80

pioneer

36a43

141.4

volkers

147.1067

-0.03879

-0.04748

-0.04748

0.007579

98

pioneer

37m81

138.7

harry

150.4143

-0.07788

-0.055

-0.055

0.06356

95

pioneer

38r21

148.8

hecla

157.61

-0.0559

-0.09036

-0.09036

0.03156

97

pioneer

3893

143

hecla

157.61

-0.0927

-0.10405

-0.10405

0.024995

78

pioneer

3733

133.6

volkers

147.1067

-0.09182

-0.10838

-0.10838

0.020154

Step 8.
Calculate how good each farmer did at selecting top varities.
Sort by column A to get the data back to it’s original order. Use column K to calculate the average of the indexes from column I. This is an interesting Statistic for it evaluates a farmers ability to pick the top varieties to include in his on farm research trial..

Comments
This analysis used as it’s evaluation criteria yield corrected to 15.5% moisture alone. With many marketing options available to producers, this criteria is one of the many that could be used. If your markets and marketing strategy indicates that dockage for high moisture content should be included, this criteria can easily be incorporated into this analysis.

Web Sites

www.sdwg.com
http://yieldsummary.monsanto.com/summaryreports/
www.asgrowanddekalb.com/layout/default.asp
http://www.croplan.com
http://plant.sci.sdstate.edu/varietytrials
http://croptesting.iastate.edu/corn/06corntest.screen.pdf
www.maes.umn.edu
www.varietytest.unl.edu
www.monsanto.com
www.curryseed.com
www.pioneer.com/yield/roundup_ready_corn.htm