Reprinted from the Soil Science Society of America Journal
Volume 59, no. 3, May-June 1995
677 South Segoe Rd., Madison, WI 53711 USA

Spatio-Temporal Variability of Soil Temperature within Three Land Areas
Exposed to Different Tillage Systems
B. P. Mohanty, * W. M. Klittich, R. Horton, and M. Th. van Genuchten
ABSTRACTal., 1982). Moreover, tillage-based distributions of crop
residues left on the soil surface or incorporated in thesurface-soil horizon may also result in a nonuniform
radiation load (Potter et al., 1987; Chung and Horton,
1987; Kluitenberg and Horton, 1990; Horton et al.,1994). Crop residues usually have reflective and conductive properties that differ from mineral soil, with concurrent changes in the surface net-radiation and soil heat-flux
density. Several researchers have shown that crop residue
and other surface mulches modify mineral-soil temperature (Burrows and Larson, 1962; Cruse et al., 1982;
Radke, 1982; Gupta et al., 1982; Gupta et al., 1984;
Bristow and Abrecht, 1989), but few studies have addressed the variability of soil temperature with and without nonuniformly distributed surface residues. Other factors that affect the measured field variability of soil
temperature are the placement of sensors, both horizontally and vertically, the method of installation (Wierenga
et al., 1982), and sensor sample size (ten Berge et al.,1983).
While most researchers are aware of possible lateralvariabilities in soil temperature measurements (e.g., Kluitenberg and Horton, 1990), few have tried to quantify
this variability (Wierenga et al., 1982; Vauclin et al.,1982). Hatfield et al. (1982, 1984) and Yates et al.
(1988) studied the spatial correlation of remotely sensed
surface soil temperature data obtained with infrared thermometers. Scharringa (1976) published perhaps the mostcomprehensive study to date on subsurface soil temperature variability. After using copper constantan thermocouples in a 5 by 5 grid with a spacing of 4 m, Scharringa
reported standard deviations of the mean for soil temperature measurements in a carefully leveled and preparedsoil. He showed that the standard deviation varied withtime of year, time of day, and depth. While the variabilityof soil temperature should change with time because ofsolar-radiation changes on both a diurnal and yearly scale
and because of continuously changing water contents,
Scharringa (1976) showed that this variability decreased
with depth. For example, the standard deviation (0) of
the mean for the month of June was reported to be 1.209,
0.752, 0.628, 0.516, and 0.465”C at the 0.05-, 0.10-,
0.20-, 0.50-, and l-m depths, respectively, at 13 h. Thissame progression in o was evident during the spring andsummer months and for most of the fall and wintermonths. Scharringa concluded that a single measurementof soil temperature could not satisfactorily estimate theaverage soil temperature in his plots at any depth. A
logical extension of this important work is to examine
the spatial and temporal variability of soil temperature

The rational (lateral) spacing between soil temperature sensors to
obtain spatial independence of measurements in the field under various
tillage systems is not well studied. In particular, properly positioning
sensors in a tillage plot requires knowledge of the spatial dependence ofthe measurements. This study was conducted to measure the horizontal
spatial variability of soil temperature and its diurnal fluctuation inthree different tillage systems. Soil temperature was measured withcopper-constantan thermocouples at 49 positions in each system at
the I- and 10-cm depths. The thermocouples formed a 7 by 7 gridwith a spacing of 3.0 (east-west) by I.5 m (north-south). The threemanagement systems were fall moldboard plowing followed by spring
disking and planter operation (MP), fall chisel plowing followed byspring disking and planter operation (CP), and ridge tillage slot planting (RN). Each management system had been under continuous fieldcorn (Zea mays L.) for 8 yr. A novel finding of the study is that ahysteresis within the diurnal cycle existed between the mean and thesample variance. This hysteresis was presumably due to differencesin soil surface heating and cooling rates during the diurnal cycle.
Although no definitive relationships could be determined, semivariograms appeared to show a time dependence and anisotropy during
the 24-h observation period. Correlation distances were = 9 m in theE-W direction along crop rows and tillage operations but <1.5 m in
the N-S direction across crop rows and tillage operations. These spatial
correlation distances will be useful in deciding the minimum (lateral)
spacing between adjacent soil temperature sensors in the field.

soil temperature is perhaps theAmost important transient physical property of soil toaffect crop growth. Research shows that soil temperature
FTER

SOIL

WATER,

affects both the rate and thoroughness with which a plantroot system permeates soil (Kaspar and Bland, 1992,
and references therein). Several factors influence thevariability of soil temperature in the field (Shumway etal., 1989; Davidoff and Selim, 1988). Tillage affects
both the surface microtopography and the subsurface
soil thermal properties. Changes in surface microrelief
can greatly influence the local radiation characteristics,
and hence the heat balance, because of changes in surface
orientation and surface slope (Benjamin et al., 1990).
Local soil orientation may or may not be toward thesun. Possible nonuniform radiation loads at the surface
may affect not only diurnal, vertical soil temperaturefluctuations (de Vries, 1963; Papendick et al., 1973;
Persaud and Chang, 1983; Horton and Wierenga, 1983;
Horton et al., 1984; Gupta et al., 1984; Pikul, 1991;
Kemp et al., 1992; Katul and Parlange, 1993) but also
the horizontal variability in soil temperature (Vauclin etB.P. Mohanty and M Th. van Genuchten, U.S. Salinity Lab., USDAARS, 450 Big Springs Rd., Riverside, CA 92507; and W.M. Klittich
and R. Horton, Agronomy Dep., Iowa State Univ., Ames, IA 50011.
Contribution as journal paper no. J-15842 of the Iowa Agric. and Home
Economics Exp. Stn., Ames, IA. Projects no. 2556 and 2715. Received
23 May 1994. *Corresponding author (bmohanty@ucracl.ucr.edu).

Abbreviations: MP, moldboard plowing followed by spring disking andplanter operation; CP, fall chisel plowing followed by spring disking andplanter operation; RN, ridge tillage slot planting; N, north; S, south; E,
east; W, west; LSD, least significant difference.

Published in Soil Sci. Soc. Am. J. 59:752-759 (1995).

752

MOHANTY ET AL.. SPATIO-TEMPORAL VARIABILITY OF SOIL TEMPERATURE

for soils with nonuniform surface configurations anddifferent surface-residue covers.
Soil surface condition is one feature that farmers canreadily change through the implementation of different
management systems. For example, Gupta et al. (1983)
showed that the amount of surface residue can have asignificant effect on soil temperature. Potter et al. (1985)
similarly showed that soil thermal conductivity, andhence, soil temperature, can be altered by tillage. Accurate measurement of soil temperature under various tillage systems is not easy. In a review of the effect oftillage on soil temperature, Wierenga et al. (1982) statedthat the horizontal or vertical placement of sensors, thenumber of sensors, and their method of installation, cansignificantly affect the measurements, particularly in theupper 5 cm of soil.
Variography and autocorrelation have been used extensively for studying the spatial variability of soil temperature (Folorunso and Rolston, 1984; Vauclin et al., 1982;
Webster and Burgess, 1983; Davidoff et al., 1986; Yates
et al., 1988). Of special interest to us is a study by Vauclin
et al. (1982), who determined the spatial correlation ofsoil surface temperature along two transects in bare fieldsoil immediately following irrigation. Measurements ofsoil surface temperature by infrared thermometry wereobtained along a 60-m transect in the N-S direction anda 100-m transect in the E-W direction at solar noon forthree consecutive days after a 0. l-m sprinkler irrigation.
Using variography, they found that soil surface temperature was correlated with an average distance of 13 m in
the E-W transect and 12 m in the N-S transect. Because
the data represented only one realization of soil surface
temperature in the diurnal cycle, Vauclin et al. (1982)
could not determine if the correlation distances changed
with time.
The objective of this study was to determine the horizontal spatial correlation of field soil temperatures during
a diurnal cycle. Soil temperature data were collected atl- and 10-cm depths in three tillage plots and analyzed fornormality and uniformity of sample variance throughout a
24-h period. Semivariograms were used to obtain, during
the diurnal cycle, the range of horizontal-spatial correlation in observed soil temperature measurements in thefield plots. The information should lead to optimal spacing between adjoining soil temperature sensors in fieldsoils and further understanding of spatial variation insoil temperature under different tillage-management
practices.
METHODS
Three field plots with different tillage histories were used.
The plots were located at the Agronomy and Agric. Engineering
Res. Center of Iowa State Univ., 13 km west of Ames, IA.
The soil is classified as a Nicollet (fine silty, mixed, mesic,
Aquic Hapludoll). The three tillage treatments included MP,
CP, and RN. Each plot had been maintained under continuous
field corn for 8 yr. The planter operation was the same as
during previous years, except that no seeds were dropped into
the soil when the planter was pulled through the field. The

753

Fig. 1. Field-plot diagram showing thermocouple locations. The
planter and tillage operation was in the East-West direction.

planter rows were spaced 0.75 m apart and ran in theE-W direction.
Copper-constantan thermocouples placed at l- and 10-cm
depths were located at 49 positions within each tillage fieldplot. The thermocouple locations formed a 7 by 7 grid, withspacings of 3.0 by 1.5 m in the E-W and N-S directions,
respectively (Fig. 1). The fields were maintained free of vegetation during the measurements. The thermocouples were locatedwithin the planter rows and were allowed to equilibrate withthe soil environment for 2 wk. Hourly soil temperature observations were recorded following soil moisture redistribution aftera rain event. Because the data were not taken concurrently
across tillage plots, no direct comparisons could be madebetween the three tillage systems. Hence, all data will beanalyzed and interpreted mainly for variability within the individual tillage systems. We selected data representing one cloudless day from each tillage plot: 26 May for MP, 8 June forCP, and 21 June for RN. The three days had similar global
solar radiation and rainfall histories.
Thermocouples were connected to a data-logger 1 (Model
CR5, Campbell Scientific, Logan, UT), which recorded hourly
temperature values. The thermocouples were constructed of
24-gauge copper-constantan wires joined with 60/40 rosin-coresolder and sealed with epoxy. The junctions were tested toensure environmental isolation and proper functioning. After
excavating a short, narrow, and = 1 l-cm-deep trench, thethermocouples were installed beginning near the planter row
and extending away from the row, leaving an undisturbed soil
face near the planter row. The thermocouples were pushed= 2 cm horizontally into the undisturbed soil face of the trench
until the sensor was in the planter row. A template was used
to ensure that the depth from the surface and the distance
between thermocouples remained the same. Lead wires were
fastened to the bottom of the trench, leaving 20 cm of coiled
lead in the bottom of the trench to minimize thermal conduction
from the surface. Finally, the trench was carefully backtilled
to its approximate original bulk density, and displaced plant
residue was placed back on the soil surface. After obtaining
the soil temperature data, soil bulk densities at the thermocouple
sensor sites were measured at depths of 1.0 and 8.5 cm using
an Uhland core sampler (Blake, 1965).
The observed soil temperature data were analyzed by meansof Kolomogorov-Smirnov test statistics (Steel and Torrie,1980) to give the relationship between the sample-order andnormal-order statistics. This method was used to investigate
1 Mention of trade names does not constitute an endorsement.

757

MOHANTY ET AL.: SPATIO-TEMPORAL VARIABILITY OF SOIL TEMPERATURE

Table 2. Kolmogorov-Smirnov test statistics @) for the diurnal
maximum (MAX), average (AVG), and minimum (MIN) temperature values (“C) at l- and 10-cm depths on three tillage
plots. In all cases, the number of samples is equal to 49.
Diurnaltemperature

l-cm

10-cm
depth

depth
Moldboard plowing
0.073
0.082
0.100
Chisel plowing
0.113
0.075
0.140*
Ridge no till
0.076
0.143
0.200*

MAX
AVG
MIN
MAX
AVG
MIN

M
MIN

vations at each hour individually, no significant correlation was found to exist between the means and variancesalong the E-W or N-S direction. Thus, the intrinsichypothesis assumption was satisfied, and use of the semivariogram was validated.
Rather than presenting individual semivariograms foreach tillage treatment, hour, depth, and/or direction combination, we decided to categorize the semivariograms
into three basic shapes: viz, pure nugget (Type I), linear(Type II), and spherical (Type III). Tables 3 and 4
present a summary of the semivariogram types and thecorresponding correlation distances. The information inthese tables indicates that the semivariograms are neither
constant, nor seem to occur in a random manner. The
MP semivariograms in the E-W direction show strong
consistency with respect to type and correlation distance,
whereas the CP and RN systems show more variability;
however, similar types (shapes) of semivariograms appeared during certain parts of the day. For example, theE-W direction semivariograms from the MP l-cm depth
are all of Type III from Hours 1 through 7 and 14
through 24 and of Type II from Hours 8 through 13.
We therefore found a time dependency in the type andcorrelation distance. This conclusion is supported by ourearlier finding that the data set is time dependent, having
a larger variance during heating than during cooling at
the same mean temperature. Although there is an obvious
time dependency, the manner in which the spatial correlation varies with time cannot be readily deduced fromthe data in Tables 3 and 4.
Data from the MP field at the l-cm depth showedcorrelation distances of =9 m in the E-W direction,

0.114
0.104
0.203*
0.125
0.118
0.115
0.079
0.064
0.194** Hypothesis of normal distribution is rejected at 0.05 probability level.
Sample statistics were used to calculate the critical values of fl.

show differences between the transformed and untransformed semivariograms. Appropriate semivariogramsfrom the CP field and from the l-cm depth of the RN
field were estimated from the transformed data, i.e.,
after removing the row or column means.
Our initial investigation of the data showed a significant
correlation between the mean and variance with respect
to time. The relationship was shown to be hysteretic
during each 24-h period. The presence of hysteresis
made it impossible for us to combine the data with time
to form one daily average semivariogram as was done
previously by Hamlett et al. (1986) for his daily soil
water tension data. However, when we examined obser-

Table 3. Summary of semivariogram shapes and spatial correlation distances for the E-W direction at the 1- and 10-cm depths. Shapes
are given three general forms. Type I displays a pure nugget effect, Type II increases but shows no sill, and Type III increases to asill. Correlation distances are given in lags.?
Tillage system*
MP

CP

1 cm
Hour

Type

1
2
3
4
5
6

III
III
III
III
III
III
III
II
II
II
II
II

:
9
10
11
12
13
14
15
16
17
18
19
iL!
2:
24

I’I’I
III
III
III
III
III
III
III
III
III
III

10 cm
Lag
:
:
:
3
+
+
+
+
+
+
3
:
:
3
3
:
3
3

Type
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
III
II
II

RN

1 cm
Lag
:
:
3
3
:

Type
III
III
III
III
III
III

:
3

I:1
III
III
III
III
III
III
III
III
I
I

:
3
3
+
+

:
III
III
III
III

:
3
:
3
3

10 cm
Lag
:
3
3
3
3
5

Type
III
III
I’I
I
II
III
III
II
II

:
:
3
3
3

::
I
I

1 cm
Lag

Lag

Type

:

:

+

:

+
4
4
+
+
+
+

:

:
I
III
III
I

:
I
I

1:1
III
III
III
I
I

:
I
:

:
I

:
:

:
I
I:1

Lag

I

:
:
3
3

Type

10 cm

3

:
I

:
III
III
II

:
+

:
I

I:1

3

:

t 1 lag = 3 m, 2 lags = 6 m, 3 lags = 9 m, and 4 lags = 12 m, + = 4 lags (12 m) or more, - = correlation is unknown and may be nonexistent.
$ MP = moldboard plowing; CP = chisel plowing; RN = ridge no till.

4
4
3
:
3

758

SOIL SCI. SOC. AM. J., VOL. 59, MAY-JUNE 1995

Table 4. Summary of semivariogram shapes and spatial correlation distances for the N-S direction at the l- and IO-cm depths. Shapes
are given three general forms, Type I displays a pure nugget effect, Type II increases but shows no sill, and Type III increases to asill. Correlation distances are given in 1ags.t
TiIIage system$
MP
1 cm
Type

1

I
I

I
I

:

:

:
I
I
I
I
I
I
I
I
I
I
I
I
I
I
III
III
I
I

:
I

5
4
5
6
8’
9
10
11
12
13
14
1s
16
17
18
::
21
2’3
24

1 cm

10 cm

Hour

Lag

Type

I:1
III
I
I
I

2
3

RN

CP

:
I
III
III
III
III
III
III
III
III

Lag

Type

:

III
III
III
III
III
III
I
I
I
I
I
I
I
I
I
I
I
II
III
III

:
:
2
3
:

:
I
I

Lag
i:
:
3
3

Type

Lag

Type

III
III
III
III

2

I

;
2

:
I
I

1:1
I
III
I
I
I

3
3

:
I

+
:

:
I
I
I
I
I
I
II
III

10 cm

1 cm

10 cm

-

+
3

r’
I
I
I
I
I
I
:
I
I
I
I
I
III
III
III
I

Lag

Type

+

I
I
I
I
I
I
I

Lag

:
I
I
:
I
:
I
I

:
3

:
I
I
I
I

t 1 lag = 1.5 m, 2 lags = 3 m, 3 lags = 4.5 m, and 4 lags = 6 m, + = 4 lags (6 m) or more, - = correlation is unknown and may be nonexistent.
$ MP = moldboard plowing; CP = chisel plowing; RN = ridge no tiU.

with slightly greater correlation distances during Hours
8 to 13. The correlation distance at the 10-cm depth was
mostly 9 m, with 2 semivariograms (for Hour 23 and24 ) showing a greater correlation distance. The semivariograms in the E-W direction for the CP data at l-cm
depth showed spatial correlation until a distance of 9 m
for most of the diurnal cycle, whereas those for the
10-cm depth were highly variable. The RN data revealed
several Type I semivariograms in the E-W direction. A
group of semivariograms for the lo-cm-depth data in
the RN field during Hours 5 through 12 (excluding 7
and 8) showed spatial correlation distances of 9 to 12 m.
The semivariograms for the N-S direction were foundto be very consistent. Type I semivariograms dominated
all three tillage-plots in the N-S direction, indicatingthat the horizontal spatial-correlation distance was either<1.5 m (1 lag) or >6 m (4 lags). Some similarities
between MP and CP fields were found in terms of their
spatial structures. A group of Type III semivariograms
between Hours 17 and 24 existed for the lo-cm-depth
data in the MP field. The CP field showed correlation
at both the l- and 10-cm depths between Hours 1 and6. Correlation distances in the N-S direction tended tobe <1.5 m (1 lag), indicating that correlation may haveexisted at intervals shorter than those used in this study(1.5 m).
The study shows that the semivariograms in the E-W
and N-S directions are largely anisotropic. Semivariograms in the E-W direction showed more spatial structure
than those in the N-S direction. Because the MP and
CP plots exhibited some spatial structure, while the RN
plot was essentially structureless, the anisotropy may

have been a result of the tillage operations. The resultsof Vauclin et al. (1982) support the existence of spatial
correlation for the soil used in their study. Cressie andHorton (1987) and Mohanty and Kanwar (1994) concluded that plowing makes a soil more uniform andthus should contribute to increased horizontal spatial
correlation of soil properties. Likewise, this study indicates that the spatial correlation of soil temperature is afunction of time; the time-dependency is likely a functionof the involved tillage operation.
Using the knowledge of spatial correlation, we now
can optimize the lateral spacing of sensors for the measurement of soil temperature in a field plot. In plots thatare anisotropic, it would be beneficial to either place thesensors more than 9 m apart in the direction of tillage
or to place the sensors more than 1.5 m apart perpendicular to the tillage direction.
CONCLUDING REMARKSOne of the most important conclusions of this study
was that a hysteresis existed between mean and varianceof the hourly soil temperature under all three tillage
practices (CP, RN, and MP) and both depths (1 and 10cm). The hysteresis is possibly due to differences insoil-surface heating and cooling rates during the diurnal
cycle. Spatial and temporal variability of surface energy
and subsurface mechanisms of heat transfer affect soil
heating and cooling rates. Hourly semivariograms of soil
temperature for three management systems displayed atime dependency as manifested by the presence of similar
semivariogram types during certain parts of the diurnal

MOHANTY ET AL.: SPATIO-TEMPORAL VARIABILITY OF SOIL TEMPERATURE

cycle. Semivariograms in the direction of tillage (E-W)
indicated a spatial correlation of =9 m for the MP and
CP fields and of unknown distance for the RN field.
Range of semivariograms in the N-S direction was generally <1.5 m. This anisotropy is likely the result of the
invoked tillage and planting operations.
ACKNOWLEDGMENT

Thanks to Dr. R.M. Cruse for providing the thermocouple
wire used in this study.

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