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Pasture and Hay Production Potential in West Virginia

Crop production is determined by the genetic potential of the crop species, soil quality, soil fertility, weather and harvest management. Potential forage production is the forage dry matter yield achieved under best management practices (BMP) without major environmental or management limiting factors over a period of time. Factors that limit crops from achieving their potential yield can be divided into those that can be readily managed and those that cannot be readily managed and need to be managed around (Table 1). Input costs and crop market values determine which limiting factors can be addressed in a cost-effective manner.

Table 1. Forage crop limiting factors that can and cannot be easily managed.

Can be readily managed

Cannot be readily managed

Soil pH and fertility

Harvest efficiency

Solar radiation interception and utilization

Storage and feeding efficiency

Water infiltration rate

Soil’s plant available water holding capacity

Irrigation

Ambient temperature

Rainfall

Solar radiation

Soil texture

Soil limits to rooting depth

Soil slope

Soil position in the landscape

Plant Species Differ in Yield Potential

Plants differ in their yield potential due to genetic differences in production efficiency and their response to soil and climatic factors. Years of variety trial experience in the Northeast give us a baseline for crop production potential in this environment. Most variety trials are conducted on high quality soils that are adapted to the growth of alfalfa and corn. In general, these soils are fairly level, have good rooting depth and good drainage. Grasses in these field trials are generally fertilized with 170 to 180 pounds of nitrogen per acre per year. In recent West Virginia trials, grasses were grown in combination with red clover and large type (“ladino”) white clover.

Variety trials are generally conducted at lower elevations that have warm summer temperatures (71 F to 79 F July temperature) with locations differing in annual rainfall (40 to 52 inches). The cool-season growth environmental index (which will be discussed below) for the major variety trial locations ranges from 0.68 to 0.70.

Alfalfa has the highest potential yield on these sites (Table 2). Red clover has a lower yield potential than alfalfa. However, when environmental conditions are suboptimal for alfalfa (i.e., soil depth, drainage, pH), red clover may outperform alfalfa. The yields of the two species are similar in soil and management environments that sustain 2.5 tons per acre yield (Rayburn, et al., 1998, p. 32). Average yield of tall fescue exceeds and orchardgrass approaches 5 tons DM per acre at these nitrogen fertilization rates (Table 2). Due to the nature of soils at variety trial sites, reed canarygrass does not have the opportunity to show its competitive advantage on poorly drained, wet soils. On poorly drained sites, some varieties of orchardgrass and all alfalfa varieties would die out and not be productive. White clover is not represented in variety trials and its performance has to be evaluated as a component that provides nitrogen (N) in grass stands.

Table 2. Forage yield under good management in variety trials in West Virginia and neighboring states (VA, PA, KY) with relative dry matter yield (Rel DMY) calculated relative to the yield of nitrogen fertilized orchardgrass (1.00) grown on the same site in the same year (grasses fertilized with 170 to 180 pounds of N per acre per year in split applications).

Forage Species

DMY

SD

Site years

Rel DMY

Legumes

Alfalfa

6.23

1.18

122

1.28

Red Clover

4.35

1.47

46

0.92

Grasses

Tall Fescue

5.05

1.38

55

1.07

Orchardgrass

4.80

1.22

68

1.00

Reed Canarygrass

4.78

1.46

36

0.92

Smooth Bromegrass

4.39

1.08

35

0.87

Timothy

4.25

1.24

54

0.87

Perennial Ryegrass

3.58

1.39

25

0.73

Variety trial yields do not account for on-farm harvest, storage and feeding losses. Standard coefficients for harvest efficiency for green-chop, silage and dry hay are 1.00, 0.90 and 0.75, respectively. Storage efficiency for dry hay range from 0.97 to 0.50 for dry barn storage to outside unprotected round bale storage, respectively. When pastures are rotationally grazing, about half of the forage standing crop is grazed. When farms changed from continuous to rotational grazing based on NRCS grazing plans, they doubled the days of grazing on the farm.

Soil Type and Health

Soil type has a major impact on crop growth (Rayburn and Basden, 2022). Soil quality is determined by the potential plant rooting depth within the soil profile; external and internal drainage of the soil due to landscape position and soil texture; water infiltration rate into the soil due to texture, soil health and compaction; and plant available water holding capacity of the soil due to texture, soil organic matter and soil health. The soil management groups used in the West Virginia University Extension Fertilizer Recommendation System provide a range of expected yield from 2.5 to 4.2 tons per acre per year for grass-legume hay (Table 3). These yields are comparable to the variety trial yields adjusted to 90% DM and 0.75 harvest efficiency for dry hay. Where producers are making baleage under best management practices harvest efficiency should be higher and storage losses lower resulting in higher forage recovery and utilization as indicated in the column for yields under intensive management (Table 3). Forage available for grazing is estimated at 50% utilization of potential production for rotationally stocked pasture and at half this value for continuously stocked pastures. An animal unit month (AUM) of feed is based on a 1000 lb animal consuming 25 pounds dry matter per day or 760 pounds consumed DM/month. Yields in Table 3 are based on updated West Virginia crop yields (Rayburn and Basden, 2022). Grass hay yields are for nitrogen fertilized grass under BMPs receiving 200 pounds of N per acre per year in split applications. Grass-clover hay yield is based on the legumes providing an equivalent yield of 150 pounds of N per acre per year, which is the average N equivalent grass-legume yield (0.833 relative yield). When rotationally grazing a meadow, the AUM of grazing obtained are equivalent to 1.75 AUM per ton of hay. Continuously grazed pasture provides only half the AUM of grazing as rotationally grazed pastures.

Table 3. Expected pasture and hay yield by Soil Yield Class as used in the West Virginia Fertilizer Recommendation System, pasture yield in animal unit months of grazing (AUM) and hay yield (90% DM) under best management practices.

Soil Yield Class

Pasture Continuously Stocked

Pasture Rotationally Stocked

Grass-Clover Hay

Grass Hay

N-Fertilized

Alfalfa Hay

Yield Units

AUM/Ac

Ton/Ac

I

3.7

7.4

4.2

5.0

6.0

II

3.3

6.6

3.7

4.5

5.0

III

3.0

6.0

3.4

4.1

4.5

IV

2.6

5.3

3.0

3.6

4.0

V

2.2

4.4

2.5

3.0

3.5

Soil Fertility

Soil Fertility has a major impact on forage growth. Soil fertility can be measured through soil testing for soil pH, phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S) and trace minerals. Soil nitrogen (N) status is not measured in the soil testing laboratory. Crop response to fertilizer N application is measured in field trials and recommendations are based on those field measurements. Soil test values for nutrients other than N are assigned relative values of low, medium, optimum and excess, which indicate the relative crop yield expected if no addition of the nutrient is added through fertilization (Figure 1).

The soil test nutrient sufficiency value is an important number for making management decisions. This is the break point between the medium and optimum soil test levels. At this point, the addition of more nutrient will not increase crop yield. However, where crops are removed from the field, the addition of nutrients at the crop removal rate is needed to maintain soil fertility. The number used for the sufficiency or critical value depends on the nutrient being discussed and the extraction method used in the laboratory. For the current WVU Soil Testing Lab Mehlich 3 extraction, these values are 30 ppm P, 90 ppm K and 150 ppm Mg.

Plant growth response (fraction of relative yield) to soil test levels. The break between medium and optimum indicates soil test nutrient sufficiency. Beyond optimum is the excess region. Dark vertical lines indicate break points between soil test levels.

Figure 1. Plant growth response (fraction of relative yield) to soil test levels. The break between medium and optimum indicates soil test nutrient sufficiency. Beyond optimum is the excess region where there may be plant health or environmental issues. Dark vertical lines indicate break points between soil test levels.

Soil pH critical values depend on the legume being managed. Clovers require a pH of 6.0 or greater for maximum yield. Alfalfa requires a pH of 6.5 or greater for maximum yield. When pH drops below 5.8, alfalfa yields will drop to about 0.75 of potential yield. When pH is below 5.5, aluminum availability in the soil increases. This allows aluminum to fix with P, making the P less available to plants. On highly acid soils, adding P will give a plant growth response, but the availability of residual P will be short lived. Therefore, in general, lime soils have a pH below 6.0 in the top 2 inches of the soil prior to applying P fertilizer.

Soil testing tells us if a given plant nutrient or soil pH is likely to reduce production. For example, a grass-legume stand on soil testing low in phosphorus and potassium is not likely to respond to nitrogen fertilization but will respond to P and K fertilization (Figure 2, Templeton and Taylor, 1966). On low fertility soils without the application of any P and K fertilizer, forage yields are only 50% of the potential yield and there is little response to applying 125 pounds of N per acre per year (0 P&K). When a small amount of P and K fertilization was provided (Low P&K Fert), there was a response to the P and K nutrients with relative yields increasing to 65% to 80% of potential yield with a good response to the N fertilizer. With a high rate of P and K fertilization, the yield again responded to the P and K nutrients with some response to high N fertilization. Overall, there was little response to N fertilization since the white clover was able to fix the N needed. This tall fescue-ladino clover stand responded well to the P and K fertilization and the clover provided enough N for the stand to produce 83% of the yield achieved when fertilized with 240 pounds of N per acre per year (small N-response with the split application of 250 pounds of N). Of five published research papers comparing grass-legume production to the same grass fertilized with N, four (20 site years) found that the grass-legume stands produced as much forage or animal gain as did the same grass fertilized with 200 pounds of N per acre per year, and the fifth (three site years) found that they produced as much as 66 to 125 pounds of N per acre per year (Figure 3). For producing cool-season forages, it is most economical to lime the soil to a pH of 6.0 to 6.5 (clover and alfalfa respectively), maintain soil test P and K in the optimum range, use legumes to provide the needed N and recycle nutrients through manure management to maintain soil fertility.

Tall fescue ladino clover relative dry matter yield (DMY) under different N, P and K fertilization practices on soils testing low in phosphorus (Rel DMY 1.0 = 4 tons per acre).

Figure 2. Tall fescue ladino clover relative dry matter yield (DMY) under different N, P and K fertilization practices on soils testing low in phosphorus (Rel DMY 1.0 = 4 tons per acre).

When no legumes are present in a cool-season grass stand, the stand will respond to N fertilization (Figure 3). Relative yield is treatment yield divided by maximum yield observed at that location that year. When legumes were used to provide N, the grass-legume stands produced relative yields equivalent to the grasses fertilized with 100 to 200 pounds of N per acre per year.

Climate and Weather

Weather during the growing season has a major impact on forage growth. Climate can be described as the average and range in rainfall and temperature across years. Forage production in any year is determined by rainfall (amount, frequency and intensity), air temperature and solar radiation (latitude, day of the year and cloud cover) (Rayburn, 2021). West Virginia has a wide range of rainfall and summer temperatures with 90% of weather stations having a 30-year July mean temperature ranging from 66 F to 77 F and annual rainfall ranging from 36 to 54 inches (Table 4).

In West Virginia, elevation and location west of, in or east of the Alleghany Plateau has a major impact on weather. July temperature is negatively correlated with elevation (-0.90) meaning that higher elevations have lower mean July temperatures (Table 5). Annual rainfall is positively correlated with elevation (0.33), meaning there is more rain at higher elevations, and negatively correlated with average July temperatures (-0.39).

Cool-season grass relative yield (Rel_DMY) response to nitrogen fertilization rate (N_rate) and when nitrogen is provided by legumes growing with the grass.

Figure 3. Cool-season grass relative yield (Rel_DMY) response to nitrogen fertilization rate (N_rate) and when nitrogen is provided by legumes growing with the grass.

Climate and weather vary greatly across West Virginia. To evaluate the effect of weather on cool-season forage growth, we can use the 30-year weather data statistics available from county weather stations to model the expected variability of rainfall and temperature on cool-season forage growth. A plant growth model was developed using the published response of forage crops to weather environment (Rayburn, 2021). This model was validated using measured pasture growth at two locations, one location rotationally grazed and one continuously grazed, for 11 site years.

Weather stations that represent the range of annual rainfall and range of average July temperatures were selected for modelling the effect of weather on cool-season forage growth in West Virginia (Table 6). Daily rainfall was predicted based on the probability of rain on a given day (Markov chain) and of the probability of the amount of rain in 24 hours when it was a day with rain (gamma function). These probability functions were calculated from the 30-year rainfall data available from each county weather station for the most recent 30-year period. The plant growth model was run for 12 years of simulated weather to provide an estimate of mean and variability of weather impact on forage production. Variability of production is expressed as the standard deviation (SD) about the mean for the 12 annual simulations. The range of 1-SD above and below the mean represents the range in which 66% of the annual yields should occur (two out of three years). In one year out of six, yields should be above the mean, plus 1-SD, and one year out of six yields should be below the mean, minus 1-SD.

Table 4.  Percentile distribution of average July temperature and total annual rainfall in West Virginia.

Percentile

July Temp

Ann Rainfall

99

77.4

59.1

95

76.8

53.8

90

75.8

51.5

80

74.8

48.6

70

74.1

46.5

60

73.6

45.8

50

73.1

45.2

40

71.8

43.3

30

70.9

41.4

20

70.2

39.9

10

68.3

37.6

5

66.4

35.5

1

63.7

32.6


Table 5. Correlation of geographic and environmental factors in West Virginia.

Latitude

Elevation

Jan Temp

July Temp

Ann Rainfall

Latitude

1.00

 

 

 

 

Elevation

-0.29

1.00

 

 

 

Jan Temp

-0.39

-0.66

1.00

 

 

July Temp

-0.02

-0.90

0.84

1.00

 

Ann Rainfall

0.05

0.33

-0.40

-0.39

1.00

Modeled plant growth was summarized across the representative stations to evaluate the average impact of total annual rainfall, average July temperature and plant available soil water (ASW) in the rooting zone on predicted yield. The resulting regression was:

DMYpred = 4.17 + 0.079 AnnRainFall – 0.046 JulyTemp + 0.37 ASW

R2 = 0.88, SDreg = 0.30, Average absolute error = 4%

DMYpred – predicted annual dry matter yield

AnnRainFall – 30-year average annual rainfall

JulyTemp – 30-year average mean July temperature

ASW – Plant available soil water holding capacity

This regression was applied to summary weather statistics of all West Virginia weather stations having 25 to 30 years of data to estimate weather impact on average relative forage production across the state (Table 6). The weather impact on relative yield ranged from 0.67 to 0.90 for 90% of the weather stations (Table 7).

Suggested Use in Grassland Planning

Grassland management is impacted by many management, environmental and economic factors. The response variables presented here can be used to evaluate the effect of environmental impact on cool-season forage production. A suggested approach is as follows:

  1. Start with the potential yield of dominant grass and legume species in the field or planned for establishment in the field (Table 2).
  2. Adjust this value to the yield expected due to the soil type’s yield class and method of harvest management (Table 3; haylage, dry hay, rotationally versus continuously grazed pastures with their harvest efficiencies of 0.90, 0.75, 0.50, and 0.25, respectively).
  3. Adjust yield further for soil fertility (pH, P, K, legume content or planned N application) that will cause yield limitations (Figure 1).
  4. Adjust for the local climate impact that differs from the base of 0.70 (base value of forage trial locations) as deemed appropriate (Table 7). For example, a farm in Barbour County (Location Relative Yield of 0.80) will probably have an expected yield 1.14 times greater than if it were in a county having a Location Relative Yield of 0.70 (0.80/0.70 = 1.14 times greater).
  5. When using animal unit days or months (AUD or AUM) use a 1,000 pounds AU consuming 25 pounds DM per day for 760 pounds DM per AUM (365/12*25=760).
  6. Adjust mechanically harvested forage yields for storage and feeding losses based on storage and feeding management from dry barn storage to round bales stored uncovered on the ground (0.95 to 0.50).

The range in elevation in West Virginia causes a range in summer temperature and annual rainfall. The climate across West Virginia results in cool-season forage growth potential at low elevations, east of the Alleghany plateau being a third less than forage growth at high elevations on the plateau. Understanding the impact of local climate, weather and soil on forage production enables good management decisions in pasture-based livestock production.

Table 6. Weather stations representing the range of annual rainfall and mean July temperatures in West Virginia used to model the effect of rainfall and summer temperatures on predicted yield (Yld pred), variability of predicted cool-season forage yield (Yld pred) expresses as the standard deviation (SD) of yield over the 12 modeled years, for three soils differing in plant available soil water holding capacity (ASW).

Site

County

Annual Rainfall

July Temp.

Elev (ft)

Lat

RYE

ASW (in.)

Yld pred

Yld pred SD

DMYpred reg

Franklin 2NE

Pendleton

35.2

70.9

1899

38.41

3

1.5

4.2

0.6

4.2

Franklin 2NE

Pendleton

35.2

70.9

1899

38.41

4

2.5

4.5

0.6

4.6

Franklin 2NE

Pendleton

35.2

70.9

1899

38.41

5

4.5

5.0

1.0

5.3

Romney 1SW

Hampshire

35.6

74.4

722

39.20

3

1.5

4.2

0.3

4.1

Romney 1SW

Hampshire

35.6

74.4

722

39.20

4

2.5

4.9

0.7

4.5

Romney 1SW

Hampshire

35.6

74.4

722

39.20

5

4.5

5.1

0.6

5.2

Lewisburg 3N

Greenbrier

40.4

71.5

2303

37.51

3

1.5

4.4

0.5

4.6

Lewisburg 3N

Greenbrier

40.4

71.5

2303

37.51

4

2.5

4.8

0.8

5.0

Lewisburg 3N

Greenbrier

40.4

71.5

2303

37.51

5

4.5

6.1

0.7

5.7

Huntington SP

Wayne

40.8

77.1

522

38.25

3

1.5

4.1

0.5

4.4

Huntington SP

Wayne

40.8

77.1

522

38.25

4

2.5

4.8

0.8

4.8

Huntington SP

Wayne

40.8

77.1

522

38.25

5

4.5

5.6

0.6

5.5

Elkins AP

Randolph

45.5

69.4

1978

38.53

3

1.5

5.2

0.5

5.1

Elkins AP

Randolph

45.5

69.4

1978

38.53

4

2.5

5.8

0.5

5.5

Elkins AP

Randolph

45.5

69.4

1978

38.53

5

4.5

6.6

0.6

6.2

Corton

Kanawha

45.9

74.8

640

38.29

3

1.5

4.9

0.6

4.9

Corton

Kanawha

45.9

74.8

640

38.29

4

2.5

5.6

0.5

5.3

Corton

Kanawha

45.9

74.8

640

38.29

5

4.5

5.9

0.7

6.0

Weston

Lewis

50.6

73.1

925

39.02

3

1.5

5.6

0.5

5.3

Weston

Lewis

50.6

73.1

925

39.02

4

2.5

5.2

0.5

5.7

Weston

Lewis

50.6

73.1

925

39.02

5

4.5

6.2

0.6

6.4

Coopers Rock SF

Preston

50.9

68.1

2280

39.41

3

1.5

5.9

0.7

5.6

Coopers Rock SF

Preston

50.9

68.1

2280

39.41

4

2.5

5.2

0.5

6.0

Coopers Rock SF

Preston

50.9

68.1

2280

39.41

5

4.5

6.6

0.5

6.7

Terra Alta 1

Preston

55.7

68.5

2631

39.27

3

1.5

5.9

0.5

6.0

Terra Alta 1

Preston

55.7

68.5

2631

39.27

4

2.5

6.5

0.6

6.3

Terra Alta 1

Preston

55.7

68.5

2631

39.27

5

4.5

7.2

0.3

7.1

 

Table 7. Weather stations in West Virginia and the predicted environmental impact on relative yield of cool-season forages (Avg Rel DMY) calculated using the modelled environmental impact regression, with location relative yield rounded to the nearest tenth. Highlighted cells indicate the range in values.

County

Weather Station

Latitude

Elevation (Feet)

Annual Rainfall

Mean Jul Temp

Avg Rel DMY

Location

Rel Yield

BARBOUR

BELINGTON

39.02

1799

48.6

70.1

0.83

0.80

BERKELEY

MARTINSBURG E WV RGNL AP

39.24

534

39.3

75.9

0.69

0.70

BOONE

MADISON 3NNW

38.06

710

47.8

74.9

0.79

0.80

BRAXTON

GASSAWAY

38.40

840

48.9

74.2

0.81

0.80

BRAXTON

SUTTON LAKE

38.39

835

49.3

75.1

0.81

0.80

BROOKE

WELLSBURG WTR TRMT PL

40.17

660

39.6

73.2

0.71

0.70

DODDRIDGE

WEST UNION 2

39.17

790

46.2

72.1

0.79

0.80

FAYETTE

HICO

38.07

2339

46.0

72.6

0.79

0.80

FAYETTE

OAK HILL

37.58

2040

46.1

71.0

0.80

0.80

GILMER

GLENVILLE

38.56

710

45.4

73.7

0.77

0.80

GRANT

BAYARD

39.16

2374

49.4

67.6

0.86

0.90

GREENBRIER

LEWISBURG 3 N

37.51

2303

40.4

71.5

0.73

0.70

GREENBRIER

WHITE SULPHUR SPRINGS

37.47

1919

40.5

71.9

0.73

0.70

HAMPSHIRE

ROMNEY 1 SW

39.20

720

35.6

74.0

0.66

0.70

HARDY

MATHIAS

38.52

1540

37.5

70.9

0.70

0.70

HARDY

MOOREFIELD 1 SSE

39.03

890

32.6

74.5

0.62

0.60

HARDY

WARDENSVILLE RM FARM

39.07

960

35.3

72.8

0.66

0.70

HARRISON

CLARKSBURG 1

39.16

990

45.2

72.9

0.78

0.80

JACKSON

RIPLEY

38.49

590

44.9

75.0

0.76

0.80

JEFFERSON

KEARNEYSVILLE

39.23

550

40.2

73.6

0.71

0.70

KANAWHA

CHARLESTON YEAGER AP

38.23

910

43.0

75.3

0.74

0.70

KANAWHA

CLENDENIN

38.29

690

48.6

74.9

0.80

0.80

KANAWHA

CORTON

38.29

640

45.9

74.8

0.77

0.80

KANAWHA

LONDON LOCKS

38.12

620

44.9

75.5

0.76

0.80

LEWIS

WESTON

39.02

925

50.6

73.1

0.84

0.80

LINCOLN

HAMLIN

38.17

680

44.3

74.6

0.76

0.80

LOGAN

LOGAN

37.52

640

47.0

76.6

0.77

0.80

MARION

FAIRMONT

39.28

1300

45.8

72.2

0.79

0.80

MARION

MANNINGTON 8 WNW

39.33

1100

48.6

71.6

0.82

0.80

MARSHALL

MOUNDSVILLE

39.54

620

41.3

73.8

0.73

0.70

MASON

HOGSETT R.C. BYRD DAM

38.41

570

41.0

74.6

0.72

0.70

MERCER

ATHENS

37.25

2494

36.7

70.6

0.70

0.70

MERCER

BLUEFIELD MERCER CO AP

37.18

2890

39.6

70.9

0.73

0.70

MINGO

WILLIAMSON

37.40

670

43.5

77.4

0.73

0.70

MINGO

WILLIAMSON

37.40

760

46.1

77.4

0.76

0.80

MONONGALIA

MORGANTOWN HART FLD

39.39

1240

42.1

73.2

0.74

0.70

MONONGALIA

MORGANTOWN L&D

39.37

825

43.3

72.9

0.75

0.80

MONROE

UNION 3 SSE

37.33

2109

35.6

70.9

0.68

0.70

MORGAN

CACAPON ST PRK # 2

39.30

950

38.4

73.9

0.69

0.70

NICHOLAS

SUMMERSVILLE LAKE

38.13

1759

47.5

70.6

0.82

0.80

OHIO

PIKE ISLAND L&D

40.09

640

38.2

73.7

0.69

0.70

PENDLETON

FRANKLIN 2 NE

38.41

1899

35.2

70.9

0.68

0.70

POCAHONTAS

BARTOW 1S

38.33

3024

43.7

66.4

0.80

0.80

POCAHONTAS

BUCKEYE

38.11

2149

45.7

69.9

0.80

0.80

POCAHONTAS

SENECA SF 1N

38.20

2450

48.0

68.2

0.84

0.80

POCAHONTAS

SNOWSHOE

38.25

4764

59.1

63.7

1.00

1.00

PRESTON

COOPERS ROCK SF

39.41

2279

50.9

68.1

0.87

0.90

PRESTON

ROWLESBURG 1

39.20

1423

55.8

70.9

0.91

0.90

PRESTON

TERRA ALTA #1

39.27

2629

55.7

68.5

0.93

0.90

PUTNAM

WINFIELD LOCKS

38.32

611

41.5

75.5

0.72

0.70

RALEIGH

BECKLEY RALEIGH CO AP

37.47

2513

41.5

70.5

0.75

0.80

RALEIGH

BECKLEY VA HOSPITAL

37.46

2329

39.4

68.8

0.74

0.70

RANDOLPH

DAILEY 1 SE

38.47

1999

48.2

68.6

0.84

0.80

RANDOLPH

ELKINS RANDOLPH CO AP

38.53

1978

45.5

69.4

0.80

0.80

RANDOLPH

GLADY 1 N

38.48

2839

52.6

66.3

0.90

0.90

RITCHIE

CAIRO

39.12

760

40.2

73.4

0.72

0.70

ROANE

SPENCER

38.48

943

45.6

73.6

0.78

0.80

SUMMERS

BLUESTONE LAKE

37.38

1390

37.8

73.4

0.69

0.70

SUMMERS

FLAT TOP

37.35

3334

46.1

68.6

0.82

0.80

TUCKER

CANAAN VALLEY

39.03

3248

51.6

65.9

0.90

0.90

TUCKER

PARSONS 1 NE

39.06

1826

51.7

70.6

0.87

0.90

TYLER

MIDDLEBOURNE 3 ESE

39.28

782

45.2

73.2

0.77

0.80

UPSHUR

BUCKHANNON

38.59

1455

48.8

71.6

0.83

0.80

WAYNE

DUNLOW 1 SW

37.57

1200

45.2

73.6

0.77

0.80

WAYNE

HUNTINGTON SEWAGE PLT

38.25

520

40.8

77.1

0.70

0.70

WAYNE

HUNTINGTON TRI STATE AP

38.22

824

42.3

75.8

0.72

0.70

WAYNE

WAYNE 2

38.14

600

43.3

76.0

0.73

0.70

WIRT

CRESTON

38.58

650

45.6

74.1

0.77

0.80

WOOD

PARKERSBURG WOOD CO AP

39.21

831

42.9

74.7

0.74

0.70

WYOMING

KOPPERSTON

37.44

1660

51.6

71.6

0.86

0.90

WYOMING

PINEVILLE

37.34

1280

46.1

73.8

0.78

0.80


Table 8. The distribution of the impact of mean annual rainfall and July temperature on simulated relative yield of cool-season forages across weather stations in West Virginia.

Percentile

Relative Location Effect

99

1.00

95

0.90

90

0.87

80

0.83

70

0.80

60

0.78

50

0.77

40

0.75

30

0.73

20

0.71

10

0.69

5

0.67

1

0.62


References

  1. Basden, T. and E. Rayburn. 2014. WVU Extension fertility recommendation tool (FRT) for small farms. WVU-ES factsheet and spreads. anr.ext.wvu.edu/forage/soil-fertility-and-its-management
  2. Rayburn, E.B. 2021. Validating a Simple Mechanistic Model That Describes Weather Impact on Pasture Growth. Plants 2021, 10, 1754. https://doi.org/10.3390/plants10091754
  3. Rayburn, E.B.; 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
  4. Templeton, W. C. and T. H. Taylor. 1966. Yield response of a tall fescue-white clover sward to fertilization with nitrogen, phosphorus, and potassium. Agron. J. 58:319-322.
  5. Rayburn, E.B., M.H. Hall, W. Murphy, and L. Vough.  1998.  Pasture production.  In  C.R. Krueger and H.B. Pionke (ed.).  Pasture Management in the Northeast - Assessing Current Technologies, Research Directions and Educational Needs.  NRAES-113.  Northeast Regional Agricultural Engineering Service.  Ithaca NY. 218 p.


Author: Ed Rayburn, Retired WVU Extension Specialist – Forage Agronomy

Last Reviewed: December 2022