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IDENTIFYING VALUE-ADDED TRAITS IN EXPANDED CROPPING SYSTEMS: AN UPDATE ON EXPLORATORY TRIALS

Josh Posner (1), Ron Doetch (2), Janet Hedtcke (3), Jon Baldock (4)

INTRODUCTION

Research from the Wisconsin Integrated Cropping Systems Trial indicates that “expanded rotations” (longer than just corn-soybean) are important for the environmental sustainability of Wisconsin agriculture.  Agronomic work is on-going for the addition of small grains, hay, and canning crops to the common corn-soybean rotation.  Many of these options are quite productive and result in lower input use.  However, due to the lower frequency of “program” crops (e.g. corn and soybeans) they are not as profitable as simpler rotations.  As a result, it is crucial to identify ways to increase the value of the crops grown in these expanded low input systems.  Our hypothesis is that expanded rotations with their increased use of organic sources of nitrogen, reduced or eliminated use of herbicides and altered soil flora and fauna, may be associated with grain traits such as altered amino acid profiles, higher protein, different starch fractions, or higher levels of nutriceuticals that would increase their nutritional value.  Apparently a number of conventional processing sectors of the market (e.g. baby food, brewers, pet food, poultry, canning industry) are already willing to pay premiums for crops with special grain characteristics.  In 2002, corn with protein greater than 7.8% received $0.08/point/bu premium and with soybeans there was a $0.01/pt/bu premium for protein+oil > 65% from poultry producers (Doetch, pers. Comm.).  In addition, the poultry and pet food industries are looking for specific amino acids like methionine, cystein and lysine that are typically low in corn.  As another example, ethanol plants have recognized that all corns are not equal in product yield.  Improved values of “fermentable” starch greatly impacts their bottom line.  One Minnesota plant currently has a list of corn varieties that will receive a $.05/bushel premium.  On site testing equipment will soon be available to test for fermentable starch.  Brewers are interested in increasing the total starch fraction in corn they purchase.  A typical plant will earn approximately $1 million per point increase in total starch. This report focuses on exploratory work, initiated in 2000, on the effect of cropping system on grain quality.

MATERIALS & METHODS

Corn and soybean grain was harvested from a number of different production plots and research trials.  All samples (whole kernels, dried to 60° F) were tested for grain quality (protein, oil, starch and kernel density) using a Foss Infratec 1229 Near Infrared Transmittance (NIT) instrument at Quality Traders Inc (QTI). The Infratec 1229 compares reflectance on 10 small samples that total approximately 1 pound.  Over 5,000 wet chemistry lab test were used in writing the protein, oil and starch calibrations resulting in standard error of predictions of 0.2%, 0.3%, and 0.5%, respectively. All values are expressed at 0% moisture.  Newer calibration for specific amino acids and fermentable starch exist, but unfortunately are not yet sufficiently well developed for rapid “load-by-load” testing at grain elevators.

RESULTS

TRIAL #1. ON-FARM SCREENING OF QTI CORN HYBRID 9664.

At the elevator in Sunrich MN, grain from producers growing QTI corn hybrid 9664 was collected for NIT analysis.  These samples came from a range of corn-based rotations and different management systems (fertilizer rates, herbicides, tillage).  In 2000, 20 environments (farm fields) were sampled, and in 2001, 33 environments were sampled.

Table 1.  On-farm corn quality data for hybrid ‘9664’.in 2000 and 2001 (1)

Year

Site

Statistics

% protein

% oil

P+O

% starch

density

2000

20 farms

MEAN

8.56

4.62

13.18

72.76

1.31

   

MIN

7.87

4.23

12.48

71.38

1.28

   

MAX

9.07

5.12

13.81

73.94

1.33

   

Stdev

0.35

0.26

0.38

0.65

0.01

               

2001

33 farms

MEAN

8.95

4.57

13.52

73.07

1.26

   

MIN

8.01

4.00

12.34

71.47

1.24

   

MAX

9.55

5.23

14.43

74.70

1.28

   

Stdev

0.42

0.26

0.52

0.62

0.01

(1) all numbers at 0% moisture

As can be seen in Table 1, the grain quality traits for hybrid QTI 9664 were actually quite stable across these 53 random “environments”.

TRIAL #2.  SOYBEAN ROTATION AND MANAGEMENT TRIAL

Soybeans from the 2001 season were collected from Joe Lauer’s long-term factorial rotation trial.  Three rotations were sampled: 1) soybeans following 5 years of corn; 2) soybeans alternated with corn every year; and, 3) the 5th year of continuous soybeans; the two tillage treatments were no-till and conventional; and the two row spacing were 7.5 and 30”. The variety of soybean was ‘Asgrow 2301’.

As can be seen in Table 2, there were statistical differences for main effect of rotations and tillage treatments.  Highest protein levels were found in continuous soybeans and highest oil in first year soybeans following corn.  Conventional tillage resulted in higher protein levels and no-till higher oil levels.  However, the change in the grain quality traits was so small as to have no economic significance.  There was no effect of row spacing and there were no interactions.

Table 2.  Soybean grain quality under different rotations or tillage treatments (Lauer’s rotation trial), 2001.

Rotation

% protein

% oil

P+O (1)

1st year soybean after 5 years of corn

34.69b

17.89a

60.44b

Sb-C-Sb-C-Sb-C

35.04ab

17.71ab

60.63ab

Continuous soybeans

35.37a

17.61b

60.90a

 

Tillage Treatment

     

No-till

34.74b

17.85a

60.45b

Conventional tillage

35.33a

17.62b

60.86a

(1) Corrected to 13% moisture. The NIT calibration on this Infratec 1229 was set up to indicate corn component value at 0.0% moisture while indicating soybean values at 13% moisture.  This is typical problem area as nutritionist normally use “as-is” values and grain trade is generally charted at to 0.0% moisture to determine premiums.

TRIAL #3. TEN CORN HYBRIDS MANAGEMENT TRIAL. 

Ten QTI hybrids were evaluated for yield and output characteristics under varying agronomic practices on land adjacent to the WICST trials at the Lakeland Agricultural Complex. The ten hybrids were planted following corn or soybeans to evaluate the effect of previous crop on yield and output characteristics.  Superimposed on this factor were two N rates (120 and 160 N) and three planting populations (33,000, 30,000, and 27,000 pl/a).  The six row plots were conventionally tilled, nitrogen was side dressed as UAN and accent + buctril (2/3 oz and 1 pt/a respectively) were applied at V6. Other fertilizer included 100 pounds of 9-23-30 applied as starter. Two rows of 25 feet were harvested with a plot combine and samples analyzed by QTI for grain quality.

As can be seen in Table 3, there was no “main effect” of previous crop (row 2), N-rate (row 5), or population (row 9) on grain characteristics, nor were there any significant interactions between hybrids and these management components (rows, 4, 7, 11).  Not surprisingly, there was a statistical difference, between hybrids for % protein and % oil (row 3).  Table 4 represents the main effect of hybrid averaged across previous crop, N rate, and population.  As can be seen, QTI 9664 (row 4) was the highest performer for oil and protein. 

Table 3.  P-values for hybrid performance and cropping system effects on grain quality measured at Elkhorn, WI, 2001.

   

------------------------------------P>F------------------------------------

Source

Df

Yield (bu/a)

% protein

% oil

% starch

density

1. Replicate

2

0.51

0.76

0.11

0.48

0.39

2. Previous crop

1

0.35

0.61

0.46

0.29

0.03

3. Hybrid

9

0.36

0.01

0.01

0.39

0.01

4. Previous crop x hybrid

9

0.41

0.12

0.57

0.42

0.44

5. N rate

1

0.10

0.46

0.59

0.40

0.26

6. Previous crop x N rate

1

0.63

0.30

0.71

0.32

0.31

7. Hybrid x N rate

9

0.36

0.69

0.68

0.46

0.31

8. Previous crop x hybrid x N rate

9

0.10

0.87

0.98

0.56

0.43

9. Population

2

0.89

0.72

0.21

0.51

0.47

10. Previous crop x population

2

0.49

0.76

0.03

0.42

0.36

11. Hybrid x population

18

0.63

0.13

0.17

0.46

0.26

12. N rate x population

2

0.44

0.10

0.61

0.35

0.43

13. Previous crop x hybrid x pop

18

0.35

0.35

0.29

0.35

0.42

14. Previous crop x N rate x pop

2

0.34

0.13

0.49

0.39

0.50

15. Hybrid x N rate x population

18

0.36

0.79

0.92

0.47

0.40

             

Coefficient of variation (%)

 

13.8

5.8

3.7

4.8

4.7


Table 4.  Corn grain output characteristics as affected by hybrid at Elkhorn, WI, 2001.

Variety

Yield (bu/a)

% protein

% oil

% starch

Density

1. QC 9620

180

8.9

4.6

72.8

1.27

2. QC 9621

179

8.8

4.6

73.2

1.26

3. QC 9651

176

8.7

4.7

73.0

1.22

4. QC 9664

171

9.1

4.9

72.6

1.27

5. EX 1640

162

8.3

4.5

73.6

1.25

6. EX 1660

155

8.9

4.5

73.4

1.27

7. EX 1702

164

8.8

4.3

73.7

1.23

8. EX 1710

186

8.5

4.6

73.3

1.24

9. EX 1732

184

8.4

4.5

71.6

1.24

10. EX 1750

165

8.6

4.9

72.9

1.24

           

MEAN

172

8.7

4.6

73.0

1.25

MIN

155

8.3

4.3

71.6

1.22

MAX

186

9.1

4.9

73.7

1.27

STDEV

10

0.2

0.2

0.6

0.02

p-value

NS

0.01

0.01

NS

0.01


TRIAL #4.  WISCONSIN INTEGRATED CROPPING SYSTEMS TRIAL:

The corn phases of the core rotation trail were harvested and analyzed for grain quality traits in 2001 and 2002 at Lakeland, and only in 2002 at Arlington

Lakeland (LAC): Results from the Lakeland site are presented in Table 5.  In both years, corn yields were very erratic due to heavy spring rains and very droughty summers creating difficult conditions for weed control (delayed rotary hoeing in the organic systems, delayed application of herbicides in the conventional systems).  In 2001, all the corn plots were planted to the same hybrid (Cargill 4111) and there were significant grain quality differences between cropping systems.  Both forage rotations had significantly higher corn yield (due to lower weed pressure), and higher protein levels (due to a richer N-environment) and grain density compared to the grain systems.  In 2002, the conventional systems were planted to hybrid Agrigold A6382 (RM=105) while the organic systems were planted to shorter cycle Growmark FS3969 (RM=95).  Among the conventional systems, the poor yields of continuous corn (CS1) probably explain the high protein levels (it was fertilized for 160 bu/a yield).  With the organic systems, our hypothesis is that N-deficiency explains the low yield and protein levels in the grain system (CS3) vs. the forage system (CS5) with its alfalfa and manure credits.  The other traits generally were not affected by cropping system.

Table 5.  Corn grain quality on WICST at Lakeland Ag. Complex, 2001 and 2002.

Year

Variety

Cropping System

Yield (bu/a)

% Protein

% Oil

% Starch

Density (g/cm3)

300 Seed wt(gm)

2001

Cargill 4111

CS1    C-C-C

CS2    C-Sb (no-till)

CS3   Organic grain

CS4    ‘Green Gold’

CS5  Organic forage

53.3

92.5

97.1

160.3

162.3

7.5

7.9

7.6

8.8

9.2

4.1

4.3

4.4

4.3

4.7

74.0

74.1

74.4

73.3

72.4

1.23

1.24

1.22

1.25

1.26

-

-

-

-

-

 

Lsd (p<0.05)

p-value

 

33.3

***

0.7

***

0.3

**

1.4

*

0.01

***

-

-

2002

A6382

CS1    C-C-C

CS2    C-Sb (NT)

CS4    ‘Green Gold’ C-A-A-A

63.5

136.7

85.1

9.4

7.1

7.2

5.4

5.5

5.5

64.5

64.0

65.7

1.25

1.28

1.25

78.5

83.0

71.3

Lsd (p<0.05)

p-value

 

37.6

***

0.7

**

0.35

NS

2.17

NS

0.05

NS

7.80

**

 

FS3969

CS3  Organic grain

CS5  Organic forage

55.8

118.8

6.6

8.5

5.5

5.1

65.0

62.7

1.26

1.30

57.0

69.5

Lsd (p<0.05)

p-value

 

29.8

***

2.0

**

0.2

***

3.6

NS

0.05

NS

5.1

***

* = p< 0.10, ** = p<0.05, *** = p<0.01


Arlington Research Station (ARS). 

The WICST plots at ARS were also used for this exploratory study in 2002.  The same two hybrids were used as at Lakeland: A6382 on the conventional systems (CS1, CS2, and CS4); and FS3969 was used for the organic systems (CS3 and CS5).  Five subsamples, each from 10 adjacent plants, were collected in each plot at harvest to study the variability of grain characteristics within plots as well as between systems.  The variance components for experimental error and sampling error were determined with the Restricted Maximum Likelihood method as implemented in JMP 4.04.

As can be seen in Table 6, A6382 had similar grain characteristics, regardless of which of the three cropping systems it was grown in. The organically managed systems did have a significant influence on %protein, % starch and density for hybrid FS3969.  The forage system was higher in these aforementioned traits likely due to the accumulated legume credits and manure application the previous fall.  The low protein in the organic grain rotation suggests a N-deficiency during grain fill, similar to the situation at Lakeland in 2002.

Table 6.  Corn grain quality on WICST at Arlington Research Station, 2002.

Variety

Cropping System

Yield (bu/a)

% protein

% oil

% starch

Density (g/cm3)

300 seed wt (gm)

A6382

CS1 C-C-C

192.2

9.3

4.0

69.4

1.26

97.0

CS2    C-Sb (no-till)

159.0

9.1

4.0

69.5

1.26

100.3

CS4    ‘Green Gold’

186.6

9.5

4.0

68.9

1.26

98.5

             

Lsd (p<0.05)

25.2

0.9

0.2

1.0

0.01

3.5

p-value

***

NS

NS

NS

NS

NS

             

FS3969

CS3 Organic grain

157.8

6.8

4.3

69.4

1.27

74.4

CS5 Organic forage

174.1

8.4

4.2

68.6

1.28

76.5

             

Lsd (p<0.05)

14.3

1.4

0.2

0.8

.00

5.4

p-value

**

**

NS

**

***

NS

* = p< 0.10, ** = p<0.05, *** = p<0.01

The results of the subsampling effort are summarized in Tables 7a and 7b.  Table 7a shows that the variability among subsamples was larger than that across blocks (also called replications in these trials) and the variance components were relatively consistent between the two hybrids.  The dominance of the sampling error is even more clear when each component is expressed as a percent of the total random variation (Table 7b).  The meaning of the dominating effect of the sampling error is that the variability within these large plots is greater than the variability between blocks (replications).  Consequently, more progress will be gained in reducing the error and detecting significant differences by increasing the number of subsamples than by increasing the number of blocks.  While more subsamples would help detect differences, once such variability is quantified, the usual procedure is to make one well mixed composite from them so there is only one sample to analyze.


Table 7a.  Experimental error and sampling error components at Arlington Research Station, 2002.

Variety

Component

 

% protein

% oil

% starch

Density (g/cm3)

300 seed wt (gm)

A6382

Experimental error

 

0.197

0

0.231

0.000009

0

Sampling error

 

0.408

0.0749

0.574

0.000050

45.5

             

FS3969

Experimental error

 

0.297

0.0027

0

0.0000006

0

Sampling error

 

0.490

0.0387

0.565

0.0000344

38.0

             

Table 7b.  Percent of random variation due to experimental and sampling error at Arlington Research Station, 2002.

Variety

Component

 

% protein

% oil

% starch

Density (g/cm3)

300 seed wt (gm)

A6382

Experimental error

 

32.5

0

28.7

15.2

0

Sampling error

 

67.5

100

71.3

84.8

100

             

FS3969

Experimental error

 

37.7

6.6

0

1.8

0

Sampling error

 

62.3

93.4

100

98.2

100


TRIAL #5 ALTERNATIVE CORN PRODUCTION ENVIRONMENTS AT ARS.

In addition to the WICST plots, ‘Agrigold A6382’ was planted on two other fields with conventional tillage at Arlington in 2002.  One field was in corn following corn and was divided in half: one side was manured (30 ton/a) and the other received anhydrous ammonia fertilizer (120#N/ac).  In a second field was corn following corn and was split into two pieces: one piece had deliberately low fertility (STP=17, STK=59) and the other had typically high fertility (STP=55, STK=212).  Recommended rates of 160 # N/acre were applied to both halves.  Five samples were collected from each strip near harvest and the data was analyzed as independent two-treatment experiments with a t-test.

Table 8.  Corn grain quality from production fields at Arlington Research Station, 2002.

Variety

Cropping System

Yield (bu/a)

% Protein

% Oil

% Starch

Density (g/cm3)

300-wt count

A6382

C on C with manure

170.7

10.1

4.0

69.3

1.264

104.7

C on C w/o manure

175.7

9.9

4.1

69.0

1.258

97.3

T-statistic

-

0.73

0.84

0.55

0

4.09

p-value

-

NS

NS

NS

NS

***

A6382

C on C high fertility (STP=55, STK=212)

185.7

9.7

3.9

69.6

1.26

101.4

C on C low fertility (STP=17, STK=59)

28.9

11.9

3.5

66.2

1.27

51.5

T-statistic

-

10.57

7.17

8.74

6.25

18.6

p-value

-

***

***

***

***

***

* = p< 0.10, ** = p<0.05, *** = p<0.01

As can be seen in Table 8, there were no differences between manured and nonmanured corn except for the 300-weight which was significantly higher under the manured treatment.  Not surprisingly, high N fertilization in a low yielding situation (very low soil P and K) resulted in elevated protein levels.

CONCLUSION

Results from this exploratory work suggest that macro grain quality factors like % protein, % oil and % starch in corn and perhaps soybeans are not particularly sensitive to cropping system.  Relatively small differences in these factors however, were observed under very varied cropping systems.  Future work will focus on selecting hybrids or varieties with known marketable characteristics (e.g. methionine levels) and see if this specific trait is enhanced by alternative cropping system


1) Professor, UW-Madison, Agronomy Dept.
2) Marketing, Quality Traders Inc. during trial, currently at Exec. Director, Michael Fields Ag. Institute, East Troy, WI
3) Research Specialist, UW-Madison, Agronomy Dept.
4) Statistician/consultant, AgStat, Verona, WI

 

 

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