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Crop Yield Relation to Soil Characteristics in West Virginia

Soil surveys, conducted by the U.S. Department of Agriculture Soil Conservation Service (USDA ASCS) and, later, USDA Natural Resources Conservation Service (NRCS) between 1959 and 1997, provide estimated crop yields by soil series, under good management. These yield estimates were established by SCS/NRCS soil survey, county USDA Agricultural Stabilization and Conservation Service (ASCS), county Extension, and state Extension staff and faculty. The SCS/NRCS, ASCS and county Extension staff provided local information on crop performance on each soil or similar soil series. At the time, due to USDA cost deficiency payment programs, yields of corn and wheat by farmer field were maintained at the local ASCS office and often verified in the field. The state Extension staff provided crop yield information from Experiment Station and on-farm demonstrations based on identified soils and similar soils.

With time, crop yields have increased due to improved genetics and management. Fertilizer and nutrient management recommendations made by WVU Extension are based on crop yield. Therefore, it is necessary to update expected crop yields by soil type to yields being achieved today in order to provide more accurate fertilizer recommendations.

For each West Virginia soil component (soil type), expected corn and hay yields, animal unit months of grazing (AUM) and soil physical description were obtained from the National Soils Information System (NASIS) (Table 1). To update yields of alfalfa hay and small grain crops, historic expected yields were extracted from published West Virginia soil surveys (Table 2). Current yields under good management on more productive soils were obtained from regional variety trials and a survey of West Virginia corn producers who maintained yield records for individual fields (Tables 3 and 4).

Expected Crop Yield Related to Soil Component Description

Regression analysis was used to determine how well the physical descriptions of soil components were related to crop yields (Table 5). Regressions found that average soil slope, available water holding capacity (AWC) in the top 100 centimeters (40 inches) of the soil, drainage class, and presence or absence of a restrictive layer consistently described NASIS expected corn and hay yields. Corn yields were within 14 bushels per acre and hay yields were within 0.59 tons per acre 67% of the time (±1 SD) with an average absolute percent error (AAPE) of 11% for corn and 15% for hay. Less well drained soils had lower expected corn and hay yields than better drained soils.

Descriptors for growing degree days, frost-free days, estimated evapotranspiration (ET), and air temperature were not significant. Annual precipitation was significant but negative. The environmental factors of annual temperature, precipitation, frost-free days, estimated evapotranspiration and growing degree days have an interaction with elevation. Given the elevation range in West Virginia, annual temperature decreases while precipitation increases as elevation increases (Table 6). This produces a negative correlation between mean air temperature and precipitation and positive correlations between mean annual air temperature and frost-free days, ET and growing degree days (Table 7).

Testing Soil Component Description for Predicting Crop Yield

To test the use of soil physical description to predict expected corn and hay yields, soil map units were divided into a regression and a test data set (Table 8). Expected crop yield was regressed against the soil’s physical properties using the regression data set. This regression was then used to predict crop yields for soils in test data set. To check the prediction accuracy, the predicted yields were compared to the expected yields in the test data set. In this test, a prediction without error has a regression slope of 1.00 and a standard deviation about the regression (SDreg) of 0.00. For corn, the predicted yield compared to the expected yield had no fixed or proportional bias (intercept and slope not significantly different from zero and 1.0 respectively) with a SDreg of 13 bushels per acre and a 10% AAPE (Table 8, Test data set). For hay the predicted compared to expected yields had no fixed or proportional bias with a SDreg of 0.61 tons per acre and a 15% AAPE.

Using Corn Yield to Estimate Other Crop Yields

Expected hay yields are closely related to expected corn yields with an intercept not significantly different from zero (Table 9). The same applied for AUM pasture yields compared to hay yields with a SDreg of 1.6 and an AAPE of 21%. Regressions without intercepts are used since the intercepts added little improvement in predictions.

Expected yields from West Virginia soil survey yield tables were used to determine how closely historical expected yields for small grains and hay crops were related to expected corn yields (Table 10). Expected wheat and oats yields were highly related to expected corn yields with only a 3 to 5 bushels per acre SD and 7% to 8% AAPE. When the intercept values were removed (assumes that small gain yields are directly proportional to corn yields) the AAPE increased by only one to  two percentage points. A similar response occurred between corn, mixed hay and alfalfa hay yields. Since including an intercept did not greatly improve the regression statistics, the no intercept relation was used.

Estimating AUM from Hay Yield

Soil physical properties did not describe expected AUM grazing yield very well. This may be due to it being more difficult to estimate on-farm AUM grazing than corn and hay yields. It is proposed that AUM of rotational grazing under best management practices (BMPs) be calculated from expected hay yields as follows.

An animal unit (AU) is 1,000 pounds of live weight of livestock. Forage dry matter (DM) intake (DMI) per AU is defined as 2.5% of body weight or 25 pounds of DM per AU day. An AU month (AUM) expands DMI for 365 days, over 12 months, to 760 pounds of DM per AUM (365/12*25=760). Hay is forage at 90% DM while DMI is forage at 100% DM. Dry hay harvest efficiency averages 75% while rotational grazing harvest efficiency averages 50%.

Forage DM available for grazing = lb/ton * DM / Harvest Efficiency = (2000 * 0.9 /0.75) = 2400

Forage DM grazed = Forage DM available for grazing * Grazing Efficiency = 2400 * 0.5 = 1200

AUM/ton hay = Forage DM grazed / Forage DM/AUM = 1200 / 760 = 1.58/ton hay

Each ton of hay yield should provide 1.58 AUM of grazing.

This is appropriate when proper rest intervals are maintained between grazing events. Inadequate rest intervals can reduce forage yield. Excessive rest intervals can reduce forage quality and growth. Under continuous grazing as little as half of this yield may be achieved. This value of 1.58 AUM per ton of hay is not significantly different from the average value calculated across WV soil components (Table 9).

Nutrient Management Planning Considerations

When planning crop nutrient recommendations, they should be based on the yield potential of the crop when grown on the dominant soil in the field. Realistic yield goals should be developed based on soil series and using the farmer’s yield records when available. When using farmer’s yield records, five years of documented yields should be used for each field.

Updating Expected Crop Yield by Soil Component

Fertilizer recommendations made by WVU Extension are based on soils being grouped into one of five yield classes. To sort soils into the five yield classes, crop yields from the NASIS data base and soil survey yield tables were summarized by quintile. The top quintile yields in the NASIS data base (Table 11) were higher than those published in the soil survey yield tables (Table 12), indicating that yields had been updated prior to entry into the NASIS. Relative quintile yields were calculated by taking the average quintile yield and dividing it by the average top quintile yield. Relative corn yield in the bottom quintile of these tables are only three percentage points different (0.54 versus 0.57).

Modern corn yields have increased by a factor of 1.70 (225 / 123 = 1.70) compared to the top quintile in the NASIS data base. Today’s hay yields have only gone up by a factor of 1.16 (5.0/4.3 = 1.16). Expected crops yields were updated in proportion to current yields under best management practices (Table 3) compared to historic yields. Other soil factors impact crop management and expected yields. These factors were defined by NRCS and WVU Extension staff and applied to the different soils.

Rules used to determine soil slope, stones and drainage impact on management and rules used to update historic crop yields:

  1. Tilled crop yields occur on soils with average slope of 25% or less
  2. Tilled crop yields occur on soils with average surface stone cover of 1% or less
  3. Hay yields occur on soils with average slope of 25% or less
  4. Hay yields occur on soils with average surface stone cover of 12% or less
  5. Pasture yields occur on soils with average slope of 55% or less
  6. Pasture yields occur on soils with average surface stone cover of 35% or less
  7. Updated corn yield = historic corn yield bushels per acre * 1.70
  8. Updated predicted corn yield = historic predicted corn yield * 1.70
  9. Updated wheat yield = 0.38 * updated predicted corn yield
  10. Updated barley = 1.25 * updated wheat yield
    1. Barley not grown on very poorly, poorly, and somewhat poorly drained soils
  11. Updated oats yield = 0.4 * updated predicted corn yield
  12. Updated grain sorghum yield = 0.7 * updated predicted corn yield
  13. Updated soybeans yield = 0.9 * updated wheat yield
  14. Updated rye yield = 0.4 * updated predicted corn yield
  15. Updated grass hay = 1.16 * tons hay yield
  16. Updated predicted hay yield = 1.16 * predicted hay yield
  17. Updated alfalfa hay = 1.28 * updated predicted hay yield
    1. Alfalfa not grown on very poorly, poorly, and somewhat poorly drained soils
  18. Updated AUM = 1.58 * Updated predicted hay yield

Environmental Risk to Nitrogen Management

Nitrogen fertilizer is a major input into many field crops. When nitrogen is below optimum, yields will be low. When nitrogen is applied prior to wet spring weather, denitrification can break down the nitrogen, so it is not available to the crop. When nitrogen is applied above crop requirements, the excess nitrogen may leach and be a risk to ground and surface water. Soil drainage has a major impact on these loses of nitrogen. A proposed ranking of risk of leaching and denitrification of applied nitrogen is provided in another document.

Summary

Historically crop yields on soils identified by map units were evaluated by local SCS/NRCS, ASCS, and Extension staff and published in soil surveys. It appears that these yields were updated and used as the basis for the NASIS crop yields as appearing in August 2020. Expected yields are closely related to soil physical properties. In observing how expected crop yields were determined for one soil survey in Virginia, little discussion was made of physical properties other than drainage classes. The updated crop yields use the historic soil map unit yield and increase it proportionally to the yield increase that has occurred due to modern genetics and management. Where a map unit does not have a historic yield, the physical properties of the soil are used to establish an expected crop yield. These yields were evaluated by NRCS fields staff who suggested minor modifications. To improve the accuracy of these soil map unit yields, it is recommended that they be evaluated in on-farm comparisons under best management practices.

Table 1. Mean and distribution of expected yield for animal unit months of grazing (AUM), corn yield (bu/acre), and mixed hay yield (tons/acre) for West Virginia soil components as reported from the NASIS as of August 2020.

Crop

N

Mean

SD

Min

Max

AUM

191

5.4

2.0

1.5

20.0

Corn

178

108

22

60

180

Mixed hay

178

3.6

1.0

0.7

8.5

SD - standard deviation, the range about the mean that contains 67% of the observations.

 

Table 2. Mean and distribution of expected crop yields across soil components as published in West Virginia County soil survey yield tables under good management.

Crop

N

Mean

SD

Min

Max

Alfalfa hay

1717

3.6

0.7

1.8

5.5

Corn

1831

92

19

45

140

Mixed hay

1491

2.9

0.6

1.5

5.0

Oats

1294

62

10

30

85

Wheat

1345

36

7

20

55

SD - standard deviation, the range about the mean that contains 67% of the observations.

 

Table 3. Expected current crop yields on better soils under best management practices based on a West Virginia farmer survey and regional variety trails and in parenthesis yields currently used in the WVU Fertilizer Recommend System for Class I soil crop yields.

Crop

Expected yield

Grain crops

Corn grain bu/a

225 (200)

Corn silage t/a

30 (25 t)

Corn silage t/a (VA est from grain bu/a)

30 @ 225

Barley bu/a

90 (100-115)

Oats bu/a

90 (80)

Rye bu/a

80

Soybeans bu/a

80 (40-50)

Sorghum, grain bu/a

(140)

Wheat bu/a

90 (64-80)

Forage crops

Cool-season grass hay (180 lb N/a)

5 (5)

Alfalfa hay

6.2 (6.0)

 

Table 4. Yield of grass species (fertilized with 180 lb N/acre), alfalfa, and red clover in variety trials in West Virginia, Virginia, Pennsylvania, and Kentucky and relative yield compared by regression (RYreg) to orchardgrass growing on the site in the same year.

Species

Site Years

Mean Yield

tons/acre

SD

RYreg

Tall Fescue

55

5.1

1.4

1.07

Orchardgrass

68

4.8

1.2

1.00

Reed Canarygrass

36

4.8

1.5

0.92

Smooth Bromegrass

35

4.4

1.1

0.87

Timothy

54

4.3

1.2

0.87

Perennial Ryegrass

25

3.6

1.4

0.73

Alfalfa

130

6.2

1.2

1.28

Red clover

40

4.4

1.5

0.92

SD - standard deviation, the range about the mean that contains 67% of the observations.


Table 5. Regressions of NASIS expected yield for Corn (bushel per acre), Hay (ton per acre) and Pasture (animal unit months of grazing, AUM per acre) vs. soil physical parameters of average slope (Slope), available water holding capacity in the top 100 cm (AWC) and presents of a restriction layer in the soil based on individual soil components (NSIS values as of August 2020).

Regression

R2

SDreg

AAPE

N

Corn = 86.5 – 1.13 Slope + 2.60 AWC100 – 8.0 Restriction + Drainage

 

Drainage:

   Moderately well        -5.7    

   Poorly                      -23.9

   Somewhat poorly    -17.9

   Very Poorly             -30.5

0.61

14

11 %

178

Hay = 1.75 – 0.025 Slope + 0.154 AWC100 + Drainage

 

Drainage:

   Moderately well      -0.28

   Somewhat poorly    -0.67

   Poorly                      -1.14                   

0.65

0.59

15%

178

 

Table 6. Effect of elevation in meters (Elev) and latitude (Lat) on climatic variables in West Virginia (temp in F, precipitation inches).

Regression

R2

SDreg

AAPE

N

Mean July temp = 125 – 0.0107 Elev – 1.24 Lat

0.90

1.0

1%

71

Mean January temp = 118 – 0.0080 Elev – 2.19 Lat

0.80

1.2

3%

71

Mean summer temp = 124 – 0.0100 Elev – 1.44 Lat

0.89

0.9

1%

71

Mean annual precipitation = 0.0073 Elev + 1.073 Lat

0.99

4.9

9%

71

Summer precipitation = 0.0027 Elev + 0.604 Lat

0.99

2.5

9%

71

Summer = 1 April through 30 September

 

Table 7 Correlation between environmental values associated with soil components in West Virginia (ET evapotranspiration).

Mean annual air temp

Mean annual precipitation

Mean annual frost-free days base 28oF

Estimated potential ET

Estimated growing degree days base 60oF

Mean annual air temp

1.00

 

 

 

 

Mean annual precipitation

-0.56

1.00

 

 

 

Mean annual frost-free days base 28oF

0.69

-0.33

1.00

 

 

Estimated potential ET

0.99

-0.56

0.63

1.00

 

Estimated growing degree days base 60oF

0.93

-0.50

0.83

0.93

1.00

 

Table 8. The regression of NASIS expected crop yield to soil physical properties for a subset of West Virginia soils (regression data set) was used to predict crop yields on other soils based on their physical properties (test data set). The predicted yields (CornYpred, HayYpred) were then compared to the reported yields (CornY, HayY) in the test data set by regression.

Regression

R2

SDreg

AAPE

N

Regression data set

CornYpred = 87.8 – 1.23 Slope + 2.44 AWC + Drainage – 9.8 Restriction

Drainage

     Moderately well       -5.5

     Somewhat poorly   -16.0

     Poorly                     -23.0

     Very poorly            -26.0

0.57

15

11%

89

HayYpred = 1.57 – 0.011 Slope + 0.164 AWC + Drainage

Drainage

     Moderately well     -0.36

     Somewhat poorly   -0.87

     Poorly                     -1.16

0.65

0.58

16%

89

Test data set

CornY = 1.02 CornYpred

0.99

13

10%

89

HayY = 1.00 HayYpred

0.97

0.61

15%

89

SDreg – Standard deviation about the regression          AAPR – Average absolute percent error

 

Table 9. Use of NSIS expected corn yield (bushel per acre) to predict hay yield (tons per acre) and hay yield to predict animal unit months of grazing (AUM).

Regression

R2

SDreg

AAPE

N

Hay = 0.0331 Corn

0.98

0.59

11%

170

AUM = 1.53 Hay

0.92

1.7

21%

174

AUM = 1.57 /ton hay by calculation n.s. from 1.53

SDreg – Standard deviation about the regression         

AAPR – Average absolute percent error

 

Table 10. Small grain and hay yield on soils relative to corn yield based on county soil surveys expected yields. (Intercepts were significant but when removed only increased average absolute error by zero to two percentage points).

Regression

R2

SDreg

AAPE

N

Wheat = 0.381 Corn

0.99

4

9%

1329

Oats = 0.658 Corn

0.99

7

9%

1271

Mixed hay = 0.0310 Corn

0.98

0.4

10%

1459

Alfalfa hay = 0.0386 Corn

0.99

0.4

8%

1656

SDreg – Standard deviation about the regression          AAPR – Average absolute percent error


Table 11. Expected, relative, and updated expected yields by quintile from National Soils Information System for West Virginia soils (based on all components within all map units).

Expected yield

Quintile

Corn

Hay

AUM

1 to 20

71

2.2

2.9

20 to 40

86

2.9

4.1

40 to 60

96

3.2

4.8

60 to 80

109

3.6

5.7

80 to 99

132

4.3

7.1

Relative yield

1 to 20

0.54

0.51

0.41

20 to 40

0.65

0.67

0.58

40 to 60

0.73

0.74

0.68

60 to 80

0.83

0.84

0.80

80 to 99

1.00

1.00

1.00

Updated expected yield under good management

1 to 20

122

2.6

3.4

20 to 40

146

3.4

4.8

40 to 60

164

3.7

5.6

60 to 80

187

4.2

6.6

80 to 99

225

5.0

8.3


Table 12. Historic, relative and updated expected yields by quintile from published West Virginia soil survey yield tables.

Quintile

Corn

Wheat

Oats

Mixed hay

Alfalfa hay

Historic expected yields

20 to 01

68

26

48

2.2

2.7

40 to 20

80

32

58

2.6

3.1

60 to 40

89

35

62

3.0

3.5

80 to 60

99

39

67

3.1

3.8

99 to 80

120

46

76

3.7

4.6

Relative yields

20 to 01

0.57

0.57

0.63

0.59

0.59

40 to 20

0.66

0.70

0.76

0.71

0.67

60 to 40

0.74

0.76

0.82

0.81

0.75

80 to 60

0.82

0.85

0.88

0.84

0.84

99 to 80

1.00

1.00

1.00

1.00

1.00

Updated expected yields

20 to 01

128

46

50

3.0

3.5

40 to 20

149

56

61

3.6

4.0

60 to 40

167

61

66

4.1

4.5

80 to 60

185

68

70

4.2

5.0

99 to 80

225

80

80

5.0

6.0


Acknowledgement

We acknowledge and thank NRCS staff members Jared Beard, Michael Jones, Aron Sattler, Tim Dilliplane, Bob Dobos and Cathy Seybold, who were instrumental in accomplishing this study. They invested valuable time in work groups and meetings, assisted in determining crops to report, yield reporting guidelines, and reviewed and helped refine draft data and yield rationale documents. They provided data tables with soil properties used in the determination of predicted component yields, and defined level of data to be reported per the need in the NRCS planning processes. They coordinated the development of NASIS calculations to enable the upload process to include the data in the Web Soil Survey and provided data map unit yields.

Selected References

  1. Crop improvement in the 21st century. 2000. Ben Miflin. Journal of Experimental Botany 51:1-8.
  2. Improved seed is a major contributor to crop yield gains and agricultural productivity. The seed industry in U.S. Agriculture. AIB-786. Economic Research Service/USDA. p. 5-6
  3. NASIS data provided in August 2020, by NRCS staff in file named West Virginia Agricultural Parameters 9_25_2020.xlsx
  4. Rayburn, E. B. and Basden, T. 2022. Comparison of Crop Yield Estimates Obtained from an Historic Expert System to the Physical Characteristics of the Soil Components, A Project Report. Agronomy 2022, 12, 765.https://doi.org/10.3390/agronomy12040765
  5. Virginia Department of Conservation and Recreation, Nutrient Management Plan Writing,  https://www.dcr.virginia.gov/soil-and-water/nmplnr
  6. West Virginia nitrogen application timing criteria based on environmental sensitivity of sites. 2013. Joshua W. Faulkner and Tom Basden. WV_CPA_FS_590_2.

Authors: Ed Rayburn, Retired WVU Extension Specialist – Forage Agronomy, and Tom Basden, WVU Extension Specialist – Nutrient Management

Last Reviewed: December 2022