This dataset is dynamically loaded from our live database. Once a survey is selected a table appears. West Virginia Base Yield Indices By Major Component" is a method of arraying the soils in West Virginia for non-irrigated commodity crop production based on their inherent soil properties. Ratings are for soils in their present condition. (Absence of an entry indicates that a crop productivity index is not assigned). Please allow a few seconds to load. For more information about the table,
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Ed Rayburn and Tom Basden, WVU-Extension, 4/28/2021
Soil surveys, conducted by USDA/SCS and latter USDA/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 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 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 each soil or similar soils.
With time crop yields have increased due to improved genetics and management. Fertilizer and nutrient management recommendations made by WVU-Extension Service are based on crop yield. Therefor it is necessary to update expected crop yields by soil type to yields being achieved today in order to provide accurate fertilizer recommendations.
For each West Virginia soil component (soil type) expected corn and hay yield and animal unit months of grazing (AUM) (Table 1) and soil physical description were obtained from the National Soils Information System (NASIS). To extend updating yields to alfalfa hay and small grain crops, 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 (Table 3 and 4).Regression analyses were run to determine how well soil component physical descriptions were related to expected crop yields (Table 5). Regressions found that average slope, available water holding capacity (AWC) in the top 100 cm, drainage class, and presence or absence of a restrictive layer consistently described NASIS expected corn and hay yield. Corn yields were within 14 bu./acre and hay yields were within 0.59 tons/acre 67-percent of the time with an average absolute percent error (AAPE) of 11 percent for corn and 15 percent for hay. Less well drained soils had lower expected corn and hay yields.
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 but 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).
To test the use of soil component 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 used to predict crop yields for soils in test data set. To check the prediction accuracy, the predicted yield was compared to the expected yield in the test data set. In this test, a perfect prediction has a regression slope of 1.00 and a SD about the regression (SDreg) of 0.00. For corn the predicted compared to the expected yield had no fixed or proportional fixed bias (intercept and slope not significantly different from zero and 1.0 respectively) with a SDreg of 13 bu/acre and a 10% AAPE. For hay the predicted compared to expected yield had no fixed or proportional fixed bias (intercept and slope not significantly different from zero and 1.0 respectively) with a SDreg of 0.61 tons/acre and a 15% AAPE.
Expected yield of hay is closely related to expected corn yield with no impact of the intercept on the relations on SDreg or AAPE (Table 9). The same applied when AUM pasture yields were compared to hay yields resulting in a SDreg of 1.6 and an AAPE of 21-percent. When the intercept was removed there was a small change in SDreg and not change in the AAPE.
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 yield with only a 3 to 5 bu/a SD and 7 to 8 percent AAPE. When the intercept values were removed which assumes that small gain yields are directly proportional to corn yields, the AAPE increased by only 1- to 2-percentage points. A similar response occurred between corn yield, mixed hay and alfalfa hay yields.
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 1000 lb live weight of livestock. Forage dry matter (DM) intake (DMI) per AU is defined as 2.5% of body weight or 25 lb DM/AU day. An AU month (AUM) expands DMI over 30.5 days/month to 762.5 lb DM/AU month. Hay is forage at 90% DM while AU intake 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 / 762.5 = 1.57/ton hay Each ton of hay yield should provide 1.57 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. Under continuous grazing as little as half of this yield may be achieved. This value of 1.57 AUM per ton of hay is not significantly different the average value across WV soil components (Table 9).
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 using farmer’s yield history, five years of documented records are needed from each field.
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 divided 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 vs 0.57).
Top quintile corn yields have increased by a factor of 1.70 (225 / 123 = 1.70) compared to the top quintile in the NASIS data base. 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) and historic yields. The rules used were:
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 water 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 Table 13.
Historically crop yields were evaluated by soil mapping units 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, as would be expected. 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 yields use the soil map unit historic 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 NRC 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.
We want to 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.
Crop improvement in the 21st century. 2000. Ben Miflin. Journal of Experimental Botany 51:1-8. 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 Virginia Department of Conservation and Recreation, Nutrient Management Plan Writing, https://www.dcr.virginia.gov/soil-and-water/nmplnr West Virginia nitrogen application timing criteria based on environmental sensitivity of sites. 2013. Joshua W. Faulkner and Tom Basden. WV_CPA_FS_590_2. NASIS data provided in August 2020, by NRCS staff in file named West Virginia Agricultural Parameters 9_25_2020.xlsx
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 grasses (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 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 (bu/a), Hay (ton/a) and Pasture (animal unit months of grazing, AUM/a) 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 SE 4.5 0.18 0.26 2.4
Drainage: Moderately well -5.7 ± 2.6 Somewhat poorly -17.9 ± 4.1 Poorly -23.9 ± 4.3 Very Poorly -30.5 ± 10.1 0.61 14 11 % 178 Hay = 1.75 – 0.025 Slope + 0.154 AWC100 + Drainage SE 0.18 0.013 0.010
Drainage: Moderately well -0.28 ± 0.11 Somewhat poorly -0.67 ± 0.18 Poorly -1.14 ± 0.18 0.65 0.59 15% 178
Table 6. Effect of elevation in meters (Elev) and latitude (Lat) on climatic variables in West Virginia (temp oF, precipitation inches). Regression R2 SDreg AAPE N Mean July temp = 125 – 0.0107 Elev – 1.24 Lat SE 6 0.0004 0.16 0.90 1.0 1% 71 Mean January temp = 118 – 0.0080 Elev – 2.19 Lat SE 8 0.0005 0.20 0.80 1.2 3% 71 Mean summer temp = 124 – 0.0100 Elev – 1.44 Lat SE 6 0.0004 0.15 0.89 0.9 1% 71 Mean annual precipitation = 0.0073 Elev + 1.073 Lat SE 0.0022 0.029 0.99 4.9 9% 71 Summer precipitation = 0.0027 Elev + 0.604 Lat SE 0.0011 0.015 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 -0.56 0.69 0.99 0.93 Mean annual precipitation -0.56 1.00 -0.33 -0.56 -0.50 Mean annual frost-free days base 28oF 0.69 -0.33 1.00 0.63 0.83 Estimated potential ET 0.99 -0.56 0.63 1.00 0.93 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 WV 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 = 0.4 + 1.02 CornYpred 0.66 13 10% 89 CornY = 1.02 CornYpred 0.99 13 10% 89 HayY = 0.30 + 0.93 HayYpred 0.64 0.61 15% 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 (bu/acre) to predict hay yield (tons/acre) and hay yield to predict animal unit months of grazing (AUM). Regression R2 SDreg AAPE N Hay = 0.35 + 0.0300 Corn SE 0.23 0.0021 0.56 0.59 11% 170 Hay = 0.0331 Corn SE 0.0004 0.98 0.59 11% 170 AUM = 181 + 1.06 Hay SE 0.47 0.13 0.29 1.6 21% 174 AUM = 1.53 Hay SE 0.04 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 0- to 2-percentage points). Regression R2 SDreg AAPE N Wheat = 7.6 + 0.303 Corn SE 0.5 0.005 0.75 3 8% 1329 Wheat = 0.381 Corn SE 0.001 0.99 4 9% 1329 Oats = 22.6 + 0.426 Corn SE 0.8 0.008 0.69 5 7% 1271 Oats = 0.658 Corn SE 0.002 0.99 7 9% 1271 Mixed hay = 0.86 + 0.0221 Corn SE 0.04 0.0005 0.61 0.3 9% 1459 Mixed hay = 0.0310 Corn SE 0.0001 0.98 0.4 10% 1459 Alfalfa hay = 0.77 + 0.0305 Corn SE 0.04 0.0004 0.74 0.3 8% 1656 Alfalfa hay = 0.0386 Corn SE 0.0001 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 AUM in top quintile increased in proportion to hay increase
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
Table 13. Proposed environmental risk factors for nitrate leaching and denitrification based on soil drainage class (1 is low and 5 is high risk). Soil drainage class Environmental risk for nitrate leaching Environmental risk for denitrification Excessively 5 1 Somewhat excessively 4 1 Well 3 1 Moderately well 2 2 Somewhat poorly 1 3 Poorly 1 4 Very poorly 1 5