United States

Department of A Soil Temperature Model for

Agriculture

Forest Service Closed Canopied Forest Stands

u

‘;@

:*4~“,,,“,,,d

Southeastern Forest James M. Vose

Experiment Station

Research Paper Wayne T. Swank

SE-281

-
The Authors

James M. Vose is a Research Ecologist and Wayne T. Swank

is Project Leader, Coweeta Hydrologic Laboratory,

Otto, NC 28763

April 1991

Southeastern Forest Experiment Station

P.O. Box 2680

Asheville, North Carolina 28802
A Soil Temperature Model for

Closed Canopied Forest Stands

ABSTRACT

We developed a soil temperature model to predict hourly

temperatures at the litter-soil interface and at soil depths

of 0.10 m, 0.20 m, and 1.25 m in hardwood forest stands

with closed canopies. The model, which was written in

BASIC on a microcomputer, uses a numerical solution for the

partial differential heat-flow equation. Litter-soil interface

temperature was predicted from air temperature by multiple

regression. Daily temperatures at the litter-soil interface and

in the soil were predicted for 2-month periods in summer, late

fall, and early spring. Predictions were most accurate in

summer and late fall but tended to be high in late spring.

Results were in general agreement with measured values when

approximate soil thermal characteristics were used. Predicted

values were within 1 to 3 “C of measured soil temperatures.

Soil temperature prediction could be improved by using

actual thermal characteristics; however, sensitivity analyses

indicate that only large variations in thermal characteristics

significantly affect soil temperature predictions. The model

predicts soil temperatures within the bounds (f5 “C) of most

microbial activity assays and should be suitable for regulating

rate functions in nutrient and carbon cycling models.

Keywords: Computer model, soil heat flow, litter-soil interface

temperature, hourly soil temperature, seasonal variation,

BASIC.

In forest ecosystems, soil temperature regulates

many processes, such as microbial transformations

of nitrogen (Plymale and others 1987) and sulfur

(Fitzgerald and others 1983; Strickland and

Fitzgerald 1984), organic matter decomposition

(Waide and others 1988), and root growth, activity,

and respiration (Marshall and Waring 1985).

Temperatures throughout the soil profile, however, are

difficult to measure over large areas for long periods.

Thus, a soil temperature prediction model that uses

readily obtainable inputs would have great utility

for estimating or interpreting rates of ecosystem

processes. Several models are available for clearcut

forests and agricultural fields (Hanks and others 1971;

Stathers and others 1985; Wierenga and de Wit

1970), but no soil temperature models have been

developed for undisturbed forests. The objective

of our study was to develop a soil temperature

model adaptable to a variety of site conditions in

undisturbed forests.

Met hods

Changes in soil temperature over time were simulated

by using the partial differential equation:

dT/dt = d/dz[(h/Cs)(dT/dz)] (1)

where T is soil temperature, t is time, z is depth, ka

is apparent thermal conductivity in Watts meter-’

minute-l Kelvin-’ (W m-l min-’ K-l), and Cs is

volumetric heat capacity in Megajoules meter3

Kelvin-l (MJ m-3 K-l). Thermal conductivity is

a measure of the rate of heat transfer per unit of

temperature gradient. Volumetric heat capacity

is a measure of the heat storage of soil. It can be

measured directly, estimated by summing the heat

capacities of individual constituents (e.g., sand, clay,

silt), or computed as a function of bulk density

and soil water content (De Vries 1963). Thermal

diffusivity (Q) is the ratio of ka/Cs (mm2 min-l) and is

used in the numerical solution of equation (1). The

model predicts soil temperature at four depths: the

litter-soil interface and 0.10 m, 0.20 m, and 1.25 m

below the soil surface. Rather than measure the

thermal properties of our soils, we used approximate

values obtained from the literature. For heat flow

between the litter-soil interface and the 0.10-m

soil depth, an [ Y of 0.001 was used (Van Wijk and

De Vries 1963). For the 0.10-m to 0.20-m-depth

increment, we used an CY value of 0.0015; for the

0.02-m to 1.25-m-depth increment, we used an

Q value of 0.0018 (Hanks and others 1971). No

adjustments were made for changes in a with soil

temperature or moisture.

Equation (1) was solved numerically using the

Crank-Nicolson finite difference approximation

and the Thomas algorithm (Ames 1979).

Solution procedures were written in BASIC on a

microcomputer. The time step for the numerical

solution is a user-defined variable. Soil temperature

data presented in this paper were simulated with

an hourly time step. Numerical solution required
providing upper and lower boundary temperatures

and initial temperatures for each soil depth. The

litter-soil interface temperature was used as the upper

boundary. Multiple regression was used to predict

litter-soil interface temperatures. The equation was of

the form:

litter-soil temperature = a + br * Xi + bz * X2 (2)

(mean hourly in “C)

where X1 = mean hourly air temperature beneath the

forest canopy and Xz = mean (24 hr) air temperature

beneath the forest canopy of the previous day. The

X2 variable was included to account for the residual

effects of heating or cooling of the litter-soil interface

from temperatures of the previous day. Litter-soil

temperature changes due to latent heat loss and/or

radiation were not considered. The lower boundary

(> 1.25 m) was defined as the average monthly

temperature at 1.25 m. These data were obtained

from hourly measurements taken in 1983 within a

mixed hardwood stand described below. The lower

boundary temperature was held constant within each

month; however, the lower boundary temperature was

allowed to vary across months.

Data used in regression development and model

validation were from a climatic station (Coweeta

Hydrologic Lab CS-17) located in a forested

northeast-facing watershed in the Southern

Appalachians of North Carolina. Elevation of CS-17

is approximately 880 m. Overstory conditions at

the monitoring site included a deciduous forest

canopy and an evergreen shrub understory (primarily

Kalmia latifolia L.). Leaf area index (m2 rne2) of

the overstory is approximately 5.0. Temperature

at the litter-soil interface and soil temperature at

0.10 m were monitored continuously during 1983

using thermocouples and a strip chart recorder.

Air temperature was measured with a thermister

located approximately 1 m above the forest floor.

For soil temperatures at depths of 0.20 m and 1.25

m, we used data collected during 1983 from a soil

pit approximately 450 m from CS-17. Equation

(2) was parameterized using mean hourly air and

litter-soil temperature data obtained on 1 or 2

randomly selected days during each month of 1983.

The ability to generalize equation (2) was evaluated

in two ways. First, equation (2) was parameterized

using comparable data from a south-facing climatic

station (CS-21). Climatic station 21 is approximately

1 km from CS-17, is located at an elevation of

approximately 840 m, and has overstory and

understory characteristics similar to CS-17. Our

purpose here was to determine if a regression

approach, using air temperature as an independent

variable, would be appropriate (i.e., be statistically

significant and have a high R2 value) on south-facing

watersheds as well. Second, the CS-17 and CS-21

regression models were applied to data from

CS-17, and the resulting litter-soil predictions were

compared. In this assessment, similar predictions

would indicate that relationships established at one

location in the watershed would be at least generally

applicable m other locations.

Simulation required input of mean hourly air

temperature and the mean (24-hr) air temperature

of the previous day, initial temperatures for each soil

depth, and the lower boundary temperature for each

month. Model output included hourly temperatures

at the litter-soil interface and at soil depths of 0.10

m, 0.20 m, and 1.25 m, as well as 24-hour means.

Variations in soil temperature were simulated for

2-month periods. Simulations are presented for June

and July, November and December, and February and

March. Although the model predicts soil temperature

on an hourly basis, diurnal variations in *measured

temperatures were slight. Thus, simulation results are

presented as 24-hour means.

Sensitivity analyses were used to determine the

significance of: (1) the use of approximate soil

thermal properties and (2) the use of fixed ( Y values

with respect to moisture and temperature. These

analyses were performed by varying the ( Y parameters

and comparing simulation results with measured soil

temperature values. In addition, the lower boundary

temperature was varied by f3 “C to evaluate

the influence of a fixed monthly lower boundary

temperature on soil temperature at depths of 0.10,

0.20, and 1.25 m.

2
Results and Discussion

Litter-Soil Interface Prediction Equations

The regression relating mean hourly temperature

at the litter-soil interface (Y) to mean hourly air

temperature (Xi) and mean (24 hr) air temperature

of the previous day (X,) on CS-17 was highly

significant (r2=0.95, P<O.OOl, n=481). An equally

strong regression (r2=0.96, P<O.OOl, n=480)

was observed when data from CS-21 were used.

Furthermore, using the independent data set from

CS-17, we found litter-soil temperature predictions

to be in agreement when both the CS-17 and the

CS-21 regression models were used. This agreement

in temperature predictions (fig. 1) indicates that, on

a local scale, litter-soil vs. air temperature prediction

equations may be broadly applicable.

In contrast to our approach, Stathers and others

(1985) used detailed surface energy-balance equations

to predict heat flux at the soil surface (i.e., upper

boundary) in their soil temperature model for a

clearcut forest. Over a 24-hour simulation period

their model predicted surface temperature within 3

“C of measured values during the day and within 1

“C at night. However, the energy balance in forested

systems is more complex than in clearcuts because

the forest canopy influences energy input to the soil

surface (Swift 1972). Similarly, undisturbed forests

have a litter layer that, because of processes such

as insulation and latent heat loss from evaporation,

adds complexity to surface energy-balance equations.

These factors prompted an empirical approach to

predicting litter-soil temperature. Also, while perhaps

sacrificing precision, a simple empirical model reduced

meteorological data requirements. Gupta and others

(1981) used a similar approach to estimate surface

temperature in agricultural fields.

Predictions

Predicted and measured temperatures for June

and July agreed reasonably well at all soil depths

(fig. 2). Predicted values were usually within 1 to

2 “C of the measured values; however, the model

consistently overpredicted litter-soil temperature

(by as much as 2.5 “C) in early June (table 1).

Predictions at the 0.10-m soil depth were in general

agreement with measured values; errors ranged from

1.5 “C above to 2.5 “C below measured values.

Most predictions (68 percent) were within 1 OC of

measured values (table 1). Consistent with heat-flow

theory (Carslaw and Jaeger 1959), predicted soil

temperatures at 0.10 m closely paralleled measured

litter-soil temperature fluctuations. However,

measured data showed a constant soil temperature

during a 19-day time period. Because of the large

fluctuations in litter-soil temperature, we hypothesize

that the patterns shown in the measured data at

0.10 m are due to temperature probe and/or chart

recorder insensitivity. Although the model adequately

predicted soil temperature at 0.20 m in early June,

temperatures were consistently underestimated

thereafter. Seventy-two percent of the predicted soil

temperatures at 0.20 m were 1 to 2 “C lower than

measured values (table 1). Similar patterns were

observed for the 1.25-m soil depth.

Results of predicted-measured comparisons in

November and December were similar to the

results in June and July (fig. 3). Litter-soil

temperatures were predicted reasonably well by

the regression model (e.g., 70 percent of predicted

values were within 1 “C of measured values; table

l), but temperatures at the 0.10-m soil depth

were consistently underestimated by 1 to 2 “C.

At the 0.20-m soil depth, the model consistently

overestimated soil temperature by 1 to 2 “C; however,

the patterns of temperature variation predicted by

the model were consistent with measured values.

Good agreement was found between measured and

predicted temperatures at the 1.25-m soil depth.

In February and March, the litter-soil temperatures

were overestimated by the model (fig. 4). Fifty

percent of the predicted values deviated more than

2 “C from measured values (table 1). Predicted and

measured mean daily temperatures at the litter-soil

interface were 5.3 and 3.6 “, respectively. At 0.10-m

soil depth, the simulated soil temperatures followed

a pattern similar to measured values. Model values,

however, were more variable. In addition, predicted

soil temperatures at 10 cm were not in phase with

measured values (i.e., predicted temperatures

increased sooner than measured temperatures).

Soil temperature at 0.20 m was closely simulated

by the model. Although simulated values at 0.20

m were generally greater than measured values

(e.g., 69 percent of predicted values were 1 to 2 “C

greater than measured values; table l), patterns of

temperature variation over the simulation period were

3
essentially identical. At the 1.25-m soil depth, the

model consistently underestimated soil temperature

(table 1). At this depth, temperature variations were

also underestimated.

There are several possible explanations for the

differences between predicted and actual soil

temperature values. First, we used constant and

approximate soil thermal characteristics for each

soil-depth increment. Flint and Childs (1987)

demonstrated the importance of determining actual

soil thermal characteristics, and these characteristics

change with varying soil moisture and temperature.

Where more accuracy is required, actual thermal

properties should be used in the model. Second,

our validation data were spatially separate. We

assumed that the thermal characteristics of the

upper layers at CS-17 were identical to those of

the soil pit. Some of the model inaccuracies may

be related to differences in thermal properties

between the two measurement sites. Finally, because

surface temperature is probably the most critical

component in soil heat flux (Stathers and others

1985), an improved model for litter-soil prediction

may increase overall model accuracy. Despite these

sources of variability, however, the model predicted

mean daily temperatures fairly accurately. In most

cases, predictions were within 1 to 3 “C of actual

values and the patterns of daily average temperature

fluctuations were predicted accurately. Studies of

temperature regulation of microbial processes, for

example, frequently use 5 “C temperature increments

in incubation assays. Thus, the predictive capability

of the soil temperature model is within the sensitivity

bounds of biological response data, and the model

should be adequate to regulate rate functions in most

ecosystem process models.

Sensitivity Analyses

Soil temperatures in March were used to perform

sensitivity analyses. We chose March because soil

moisture is usually high in this month and thus the

approximate cr values would potentially be most

inaccurate. Hence, we expected the greatest difference

in simulated soil temperatures when ( Y values were

varied in this month. Using various combinations of

cr parameters, we compared simulation results with

the measured data. For comparison with predictions,

we used approximate ( Y values. Results reported here

are for the most dramatic parameter manipulations.

For example, figure 5A presents simulation results

when we used an cr value of 0.0001 m2 min-’ for

all depth increments. The primary result was a

reduction in the magnitude of temperature variations.

A secondary result was an alteration of the timing

of changes in soil temperature. Predictions were

improved at the 0.10-m depth but were less accurate

at the 0.20-m and 1.25-m soil depths. In contrast,

increasing the Q values to 0.0036 m2 min-l for all

depth increments (fig. 5B) increased the magnitude

of temperature variation. Predictions were less

accurate for the 0.10-m and 0.20-m soil depths but

were improved for the 1.25-m soil depth. These large

changes in ( Y parameter values resulted in, at most, an

increased overprediction of about 2 “C. Simulations

with less dramatic parameter changes yielded smaller

errors. Consistent with Stathers and others (1985)

and Hanks and others (1971), these results indicate

that approximate ( Y values will provide reasonable soil

temperature predictions.

Changing the lower boundary temperature by f3“C

had little influence (~0.5 “C difference) on soil

temperature at 0.10 m. At 0.20 m, soil temperatures

differed from the model using the actual lower

boundary temperature by about 1 “C. As expected,

changes were greatest at the 1.25-m soil depth.

Predicted soil temperature at 1.25 m was 2 “C higher

when the lower boundary temperature was increased

by 3 “C, and 2 “C lower when the lower boundary

was decreased by 3 “C.

Conclusions

Simulation results indicate that reasonable estimates

of soil temperature in forested stands can be

obtained with easily measured climatic data and

approximate soil thermal characteristics. These

properties are highly desirable in models used for

predicting soil temperature at remote locations. More

accurate estimates may be possible with the input of

site-specific estimates of soil thermal characteristics.

4
Literature Cited

Ames, William F. 1979. Numerical methods for partial

differential equations. 2d ed. New York: Academic Press.

365 pp.

Carslaw, H.S.; Jaeger, J.C. 1959. Conduction of heat in

solids. 2d ed. London: Oxford University Press. 510 pp.

De Vries, D.A. 1963. Thermal properties of solids. In: Van

Wijk, W.R., ed. Physics of plant environment. New York:

John Wiley & Sons: 210-235.

Fitzgerald, J.W.; Ash, J.T.; Strickland, T.C. [and

others]. 1983. Fo-tion of organic sulfur in forest soils:

a biologically mediated process. Canadian Journal of

Forest Research 13:1077-1082.

Flint, Alan L.; Childs, Stuart W. 1987. Field procedure

for estimating soil thermal environments. Soil Science

Society of America Journal 51:1326-1331.

Gupta, S.C.; Radke, J.K.; Larson, W.E. 1981. Predicting

temperatures of bare and residue covered soils with and

without a corn crop. Soil Science Society of America

Journal 45:405-412.

Hanks, R.J.; Austin, D.D.; Ondrechen, W.T. 1971. Soil

temperature estimation by a numerical method. Soil

Science Society of America Journal 35:665-667.

Marshall, J.D.; Waring, R.H. 1985. Predicting Sne root

production and turnover by monitoring root starch and

soil temperature. Canadian Journal of Forest Research

15:791-800.

Plymale, Andrew E.; Boerner, Ralph E.J.; Logan,

Terry J. 1987. Relative nitrogen mineralization and

nitrification in soils of two contrasting hardwood forests:

effects of site microclimate and initial chemistry. Forest

Ecology and Management 21:21-36.

Stathers, R.J.; Black, T.A.; Novak, M.D. 1985.

Modelling soil temperature in forest clearcuts

using climate station data. Agricultural and Forest

Meteorology 36:153-164.

Strickland, Timothy C.; Fitzgerald, John W. 1984.

Formation and mineralization of organic sulfur in forest

soils. Biogeochemistry 1:79-95.

Swift, Lloyd W., Jr. 1972. Effect of forest cover and

mountain physiography on the radiant energy balance.

Durham, NC: Duke University. 87 pp. Dissertation.

Van Wijk, W.R.; De Vries, D.A. 1963. Periodic

temperature variations in an homogeneous soil. In: Van

Wijk, W. Ft., ed. Physics of plant environment. New

York: John Wiley & Sons:102-140.

Waide, J.B.; Cazkey, W.H.; Todd, R.L. [and others].

1988. Changes in soil nitrogen pools and transformations

following clearcutting. In: Swank, W.T.; Crossley, D.A.,

Jr., eds. Forest hydrology and ecology at Coweeta.

Ecological Studies 66. New York: Springer-Verlag.

221-232.

Wierenga, P.J.; de Wit, C.T. 1970. Simulation of heat

transfer in soils. Soil Science Society of America Journal

34:845-848.
Table 1 -Deviation of predicted soil temperature from measured soil temperature, by month and depth.

Data are number (percent) of days when predicted values are within a given temperature deviation range.

Deviation CC)

Soil depth <-2 - 2 -1 20.5 1 2 >2

Litter-soil

interface

0.10 m

0.20 m

1.25 m

VI

Litter-soil

interface

0.10 m

0.20 m

1.25 m

Litter-soil

interface

0.10 m

0.20 m

1.25 m

4(8)

W)

I@)

WI

2(3)

10(16)

32(52)

35(57)

W)

1W4)

4(7)

16(28)

JUNE and JULY

36(60) 11(18) 12(20)

8(13) 32(52) 2(3) 5(8)

12(20) 14(23) 3(5)

14(23) 11(18) l(2)

NOVEMBER and DECEMBER

8(15) 20(38) 90 7) 7(13)

ll(21) 1W4) 4(8)

3(5) 90 7) 36(68)

w 48(91) 2(4)

FEBRUARY and MARCH

600) 60 0) 409 8(14)

90 6) 4(7) WI ll(21)

3(5) 15(26) 14(24) 26(45)

33(57) ll(21)

2(3)

5(g)

8(14)

6
28

24

4

0

,

LI

6

I I I I I I

4 a 12 16 20 24 ia

cs-17("c)

Figure l-Comparison of litter-soil temperature predictions using

south-facing (WS 2) and north-facing (WS 17) prediction equations.

7
(3) 3tlnlvt13dW31 (3) 3~nlvmdW31

0 CD ,

-.

(3) 3tlfIlW3dW31

8

(3) 3tll-uV&!3dW31
c

lO--

E

2

is

5 --

k

zz

F

0-- A Simulated data

-5 I - I

300 310 320 330 340 350 360

JULIAN DAY

ACTUAL vs. PREDICTED LITTER/SOIL TEMPERATURE

1 5

November & December 1983

ACTtIAL vs. PREDICTED SOIL TEMPERATURE ACTUAL. vs. PREDICTED SOIL TEMPERATURE

20 c m 125 cm

November & December 1983

l Actual data

A Simulated data

I 310 320 330 340 350 360

JULIAN DAY

ACTUAL vs. PREDICTED SOIL TEMPERATURE

10 cm

November & December 1983

l Actual data

A Simulated data

300 310 320 330 340 350 360

JULIAN DAY

November & December 1983

l Actual data

A Simulated data

300 310 320 330 340 350 360

JULIAN DAY

W Figure S-Litter-soil interface and soil temperature simulations for November and December.
ACTUAL vs. PREDICTED LITTER/SOIL TEMPERATURE

February & March 1983

15-

ACTUAL vs. PREDICTED SOIL TEMPERATURE

10 cm

February & March 1983

l Actuai data

G .- A Simulated data

01

3 2 5 2 7 2 9 2 3 2 5 2 7 2

JULIAN DAY JULIAN DAY

1 5

1 0

5

0

.

ACTUAL vs. PREDICTED SOIL TEMPERATURE

20 cm

February & March 1983

l Actual data

A Simulated data

1 5

ACTUAL vs. PREDICTED SOIL TEMPERATURE

125 cm

February & March 1983

l Actuai data

_ . A Simulated data

I

5 2 7 2 92 32 52 72 ,

JULIAN DAY JULIAN DAY

Figure 4-Litter-soil interface and soil temperature simulations for February and March.

2
A B

SOIL TEMPERATURE MODEL SENSITMM ANALYSIS

10 cm

12

l Actual data

2--

Oi

59 8 4 89

A Generalized alpha

o Sensitivity alpha - 0.0001

7 4 79 8 4 8 9

SOIL TEMPERATURE MODEL SENSlTlVllY ANALYSIS

10 cm

121 4

March 1983

2--

04

5 9 8 4 89

l Actua1 data

A Generalized alpha

0 Sensitivity alpha - 0.0036

7 4 7 9 8 4 8 9

JULIAN DAY JULIAN DAY

SOIL TEMPERATURE MODEL SENSlTlVlM ANALYSIS

20 cm

.- I March 1983

l Actual data

2--

OV

5 9 8 4 89

A Generalized alpha

o Sensitivity alpha - 0.0001

7 4 79 8 4 8 9

JUUAN DAY

12

10

9 8

3

is =

i 4

2

0

SOIL TEMPERATURE MODEL SENSITMTY ANALYSIS

125 cm

March 1983

8 4 8 9

l Actual data

A Generalized alpha

0 Sensitivity alpha - 0.0001

7 4 7 9 8 4 8 9

JULIAN DAY

SOIL TEMPERATURE MODEL SENSITIVITY ANALYSIS

20 cm

.- I March 1983

8

it’: : :Elkz$ : ypha :

0 Sensitivity alpha = 0.0038

5 9 8 4 89 7 4 79 84 89

JULIAN DAY

SOIL TEMPERATURE MODEL SENSlTMlY ANALYSIS

125 cm

12

March 1983

8

l Actual data

A Generalized alpha

0 Sensitivity alpha - 0.0038

04

59 84 89 74 79 84 89

JULIAN DAY

Figure 5-Soil Temperature simulations using ( Y values of 0.0001 (A) and 0.0036 (B).

1 1

I

--------------------c--------

-I

------cc----*-----------

1

I

Vose, James M.; Swank, Wayne T. 1991. A soil temperature i Vose, James M.; Swank, Wayne T. 1991. A soil temperature i

I

/

model for closed canopied forest stands. Res. Pap. SE281.

I

model for closed canopied forest stands. Res. Pap. SE281. I

Asheville, NC: U.S. Department of Agriculture, Forest Service, Asheville, NC: U.S. Department of Agriculture, Forest Service, i

Southeastern Forest Experiment Station. 11 pp. Southeastern Forest Experiment Station. 11 pp. I

I I

A microcomputer-based soil temperature model was developed to

predict temperature at the litter-soil interface and soil temperatures

at three depths (0.10 m, 0.20 m, and 1.25 m) under closed forest

canopies. Comparisons of predicted and measured soil temperatures

indicated good model performance under most conditions. When

generalized parameters describing soil thermal characteristics were

used, predicted values were generally within 1 to 3 “C of measured

values.

KEYWORDS: Computer model, soil heat. flow, litter-soil interface

temperatures, seasonal variation, hourly soil temperature, BASIC.

A microcomputer-based soil temperature model was developed to i

predict temperature at the litter-soil interface and soil temperatures

at three depths (0.10 m, 0.20 m, and 1.25 m) under closed forest I

canopies. Comparisons of predicted and measured soil temperatures

indicated good model performance under most conditions. When I

generalized parameters describing soil thermal characteristics were

used, predicted values were generally within 1 to 3 “C of measured I

values.

KEYWORDS: Computer model, soil heat flow, litter-soil interface I

temperatures, seasonal variation, hourly soil temperature, BASIC. I
The Forest Service, U.S. Department of

Agriculture, is dedicated to the principle of

multiple use management of the Nation’s forest resources

for sustained yields of wood, water, forage, wildlife, and

recreation. Through forestry research, cooperation with the

States and private forest owners, and management of the

National Forests and National Grasslands, it strives-as

directed by Congress-to provide increasingly greater

service to a growing Nation.

USDA policy prohibits discrimination because of race,

color, national origin, sex, age, religion, or handicapping

condition. Any person who believes he or she has been

discriminated against in any USDA-related activity should

immediately contact the Secretary of Agriculture,

Washington, DC 20250.