**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 > [2]**

**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 > [3]**

**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 [4]

http://yieldsummary.monsanto.com/summaryreports/ [5]

www.asgrowanddekalb.com/layout/default.asp [6]

http://www.croplan.com [7]

http://plant.sci.sdstate.edu/varietytrials [8]

http://croptesting.iastate.edu/corn/06corntest.screen.pdf [9]

www.maes.umn.edu [10]

www.varietytest.unl.edu [11]

www.monsanto.com [12]

www.curryseed.com [13]

www.pioneer.com/yield/roundup_ready_corn.htm [14]