1
Title. Landscape-scale carbon uptake and evapotranspiration from an agroforestry
system.
Authors: P.R. Ward, S.F. Micin, and I.R.P Fillery
Text pages:
25
Tables:
3
Figures:
10
Running title:
Carbon and water balance in agroforestry systems
Corresponding author: Dr. Phil Ward
CSIRO Plant Industry
Private Bag No 5
Wembley WA 6913
Australia
Phone +61 8 9333 6616
Fax +61 8 9387 8991
Email [email protected]
1
1
Landscape-scale carbon uptake and evapotranspiration from an agroforestry
2
system
3
P.R. Ward1,2, S.F. Micin1,2, I.R.P Fillery1,2
4
1
5
based Management of Dryland Salinity, University of Western Australia, 35 Stirling
6
Highway, Nedlands WA 6009, Australia.
7
Key words
8
Alley farming, carbon sequestration, dryland salinity, eddy correlation, Eucalyptus, tree
9
belt
CSIRO Plant Industry, Private Bag No 5, Wembley WA 6914, Australia. 2CRC for Plant-
10
Abstract
11
The inclusion of belts of trees in the agricultural landscape of south-western Australia is
12
gaining popularity, through perceived benefits in terms of water use, biodiversity and
13
carbon sequestration. However, water use and carbon assimilation are difficult to quantify
14
at the landscape scale. In this research, we investigate the application of eddy covariance
15
in a ‘belt and alley’ landscape using footprint modelling and an analysis of vertical and
16
horizontal wind components. Footprint modelling indicated that when sensors were
17
placed between the tree belts, the proportion of the signal from the tree belts was similar
18
to the proportion of tree belts in the landscape for most wind directions, tree heights and
19
tree leaf area indexes encountered in the experimental part of this study. Tree belts had
20
little impact on wind direction for any measurement height. Tree belts had a minor impact
21
on standard deviation of wind direction for measurement height of 3.7 m, but not for
22
measurement heights of 5.4 or 6.0 m. Persistent upward wind flows were observed for
2
1
wind directions aligned with the tree belts, suggesting the possibility of advection. We
2
then used the eddy covariance technique, before and after removing data associated with
3
winds aligned with the tree belts, to assess carbon and water balances of a field
4
containing belts of 4-years old oil mallee trees (comprising 10 m wide tree belts planted
5
60 m apart) relative to a field managed for agricultural production without tree belts. For
6
our experimental site, removal of data for winds aligned with the tree belts had a
7
negligible impact on fluxes of carbon dioxide and water, but this may not be the case at
8
other sites. At the landscape scale, the field containing oil mallees used 30 mm more
9
water over a 12-month period, and assimilated an extra 1.5 Mg CO2/ha, compared with
10
the field without trees. The results for water balance were comparable with other fields in
11
the region where herbaceous perennials had been grown, but the extra water use was
12
lower than other estimates based on sap flow measurements. Further research will be
13
necessary to determine the impact of spatial variability of water use on groundwater
14
recharge at the catchment scale. Extra carbon storage associated with tree belts may
15
increase their economic attractiveness to farmers in the region.
16
Introduction
17
South-western Australia experiences a Mediterranean-style climate, and rainfed cropping
18
predominates in the areas receiving between 300 and 500 mm of annual rainfall. In recent
19
years, environmental concerns over groundwater recharge and associated dryland salinity
20
(McFarlane et al. 2004), and loss of biodiversity (Lefroy et al. 2004), have affected
21
farming systems within the region. In particular, there has been a focus on increasing the
22
proportion of perennial vegetation in the landscape. The impact of herbaceous perennials
3
1
such as lucerne on the water balance has been extensively studied (as summarised by
2
Ward et al. 2006), and attention is now turning to woody perennials.
3
Competition for light, water and nutrients between tree belts and adjacent crops, and
4
techniques to manage it, has been well researched in recent years (e.g. Ong et al. 2002).
5
From a more hydrological perspective, the inclusion of belts of trees with the aim of
6
controlling groundwater recharge in the traditional agricultural landscape of southern
7
Australia has been theoretically studied by Stirzaker et al. (1999, 2002). In particular, the
8
inclusion of oil mallees (various Eucalyptus species) is gaining popularity, through their
9
potential for bio-energy, activated charcoal, and solvent oil production (Wu et al. 2007).
10
Wildy et al. (2004), Ellis et al. (2005) and Oliver et al. (2005) investigated the impact of
11
belts of oil mallees on water balance and crop performance. Tree belts were found to be
12
effective in reducing groundwater recharge and the subsequent threat of dryland salinity,
13
but require specific site conditions to be satisfied for their inclusion in a farming system
14
to be economically attractive (Lefroy et al. 2004; Stirzaker et al. 2002). In order to
15
calculate belt or landscape water use from sap flow data from individual trees (e.g. Wildy
16
et al. 2004; Carter et al. 2004; Ellis et al. 2005), an assumption must be made about the
17
hydrological area ‘occupied’ by the tree. The most common measurement used for this
18
calculation is the projected crown area, but recent analysis by Crosbie et al. (2008)
19
suggests that the water use by belts of trees might be overestimated by as much as 100%
20
if projected crown area is used in up-scaling calculations.
21
Another factor influencing the adoption of tree belts is their potential for carbon
22
sequestration (Nair et al. 2009), and the inclusion of such systems under the Kyoto
23
Protocol. Recent estimates of carbon sequestration for agroforestry systems in various
4
1
ecosystems range from 0.3 Mg C/ha/year in the West African Sahel (Takimoto et al.
2
2008) to more than 15 Mg C/ha/year in Puerto Rico (Parrotta 1999). Takimoto et al.
3
(2009) and Haile et al. (2008) demonstrated that alley farming led to changes in soil C
4
and N quantities, with the stable carbon in the soil derived mainly from the tree
5
components. However, as discussed by Nair et al. (2009), quantification of above-
6
ground, and more particularly below-ground biomass is difficult in agroforestry systems.
7
Furthermore, the capacity for various Eucalypt species in general, and oil mallee types in
8
particular, to sequester carbon does not appear to have been scientifically evaluated and
9
quantified under alley farming conditions.
10
The technique of eddy covariance appears well-suited to quantify the carbon and water
11
dynamics of alley farming systems, as it measures vertical fluxes of both carbon and
12
water at the landscape scale. This technique has been used for many years to assess the
13
carbon balance of many ecosystems, as reviewed by Baldocchi (2008). However, its
14
application to alley farming systems can be difficult. The structured differences in canopy
15
heights and roughness across the alley farming area can lead to complex air flow patterns,
16
which could affect fluxes measured at a point in the landscape (Sogachev et al. 2005a;
17
Mao et al. 2008). Furthermore, advection associated with temperature differences
18
between the two canopy types (Zhang et al. 2007) could result in consistent upward or
19
downward air flows at the point of measurement, which will also impact on the fluxes
20
measured at that point.
21
In this research, we aimed to determine whether the eddy covariance technique could be
22
applied to a ‘belt and alley’ landscape in order to quantify water and carbon dioxide
23
fluxes at the landscape scale. We performed a footprint analysis based on the SCADIS
5
1
model (Sogachev and Lloyd 2004; Sogachev et al. 2005b) in a simulated landscape based
2
on a field in south-western Australia, to determine the impact of tree belts on air flow
3
patterns in the landscape. We then measured patterns in horizontal and vertical wind
4
speed associated with wind direction in a field containing belts of oil mallee trees, to
5
determine (a) whether the presence of parallel tree belts caused changes in horizontal
6
wind direction, and (b) whether leaf temperature differences between the vegetation types
7
caused advection at a point between the tree belts. Finally, we used the eddy covariance
8
technique to quantify the carbon capture, and water balance, of a field containing belts of
9
trees, compared with an adjacent field with no tree belts. These results were used to
10
estimate the effectiveness of tree belts in overcoming dryland salinity, and as a sink for
11
carbon sequestration.
12
Materials and methods
13
Site details
14
The site was located on a farm in south-western Australia at 32°53’S, 117°47’E, near
15
Tincurrin, 200 km south-east of Perth. Two fields were selected on shallow gravelly
16
duplex soils, with a loamy sand A horizon of 10-15 cm depth overlying a gravelly sandy
17
clay. The first field (the ‘oil mallee’ field) was about 1000 m by 750 m, and contained 9
18
belts of oil mallees (E. polybractea) planted in 2003 in an E-W orientation, with a
19
distance of approximately 66 m between belts (Figure 1). Each belt consisted of 4 rows of
20
trees, with a total belt width of approximately 7 m. The alleys between the tree belts were
21
planted with legume pasture in 2006 (Ornithopus sativus) and 2007 (Trifolium
22
subterraneum). The second field (the ‘control’ field, about 1km east of the oil mallee
6
1
field) of similar dimensions contained no trees until seedlings at the same spacing as the
2
oil mallee field were planted in August 2007. The remainder of the area was planted to
3
wheat (Triticum aestivum) in 2007, and legume pasture (O. sativus) was sown over the
4
whole field in 2006. Rainfall during the trial was obtained from an automatic weather
5
station located in the oil mallee field. Average long-term rainfall at Dudinin, 12 km east
6
of the site, is 345 mm, of which 244 mm falls in the May-October period.
7
Eddy covariance measurements
8
One eddy covariance unit was installed in each field. Each unit consisted of a R3-50
9
sonic anemometer (Gill Instruments, UK), and a LICOR LI-7500 (LiCor, USA) open-
10
path infra-red gas analyser. Raw data was collected at a frequency of 20 Hz and stored in
11
a purpose-built logger, and subsequently analysed in 1-hour time periods with EdiRe 4.2
12
software (University of Edinburgh). Prior to flux calculations, axis rotations were
13
performed for each 1-hour period of data to force wind vectors v and w to values of zero.
14
Eddy covariance measurements were conducted for periods: May 25 – July 3, 2006; July
15
13 – August 29, 2006; September 5 – October 18, 2006; and April 4, 2007 to May 7,
16
2008. An analysis of net CO2 flux identified that a threshold value of u* = 0.2 m/s (where
17
u* is the friction velocity, and is an indicator of atmospheric mixing) was an appropriate
18
minimum value, and data where u* was less than 0.2 m/s were discarded (Goulden et al.
19
1996). Following analysis of average wind direction and average vertical wind speed
20
prior to axis rotation ( W ) (see results section), data for wind directions within 23° of
21
east or west were also discarded. Gaps in the carbon and water flux data were filled by
22
calculating the average monthly values for each hour of measurement. Average tree
7
1
height was approximately 2 m in November 2005, 3 m in October 2006, and 4 m in
2
November 2007. Measurement height in the oil mallee field was 3.7 m until July 3 2006,
3
5.4 m from July 3 2006 until October 18 2006, and 6.0 m for subsequent measurements.
4
Measurement location in the field was changed in April 2007 as indicated in Figure 1.
5
Measurement height was 1.7 m in the control field.
6
Comparison of eddy covariance units
7
According to Loescher et al. (2005), different sonic anemometers (and presumably infra-
8
red gas analysers) can have impacts on the measured fluxes of CO2 and water. In order to
9
compare the eddy covariance units, both units were installed above short, well-watered
10
grass for several days during March 2007. Measurement height was 1.8 m, and the fetch
11
was at least 40 m in all directions.
12
Footprint calculations
13
The footprint for measurements taken in the oil mallee field was analysed using SCADIS
14
software as described by Sogachev and Lloyd (2004) and Sogachev et al. (2005b). This
15
software uses numerical solutions for ensemble-averaged Navier Stokes equations to
16
calculate flux footprints for inhomogeneous vegetation. The model allows the user to
17
specify vegetation height, leaf area index (LAI) and leaf distribution with height for
18
various distances from the point of measurement. A repeating vegetation pattern of a 10
19
m belt of trees followed by a 60 m alley of short crop or pasture was set up for a distance
20
of 500 m, with the point of measurement located 30 m from a tree row (that is, close to
21
the mid-point between tree rows). This approximates the situation experienced in the oil
22
mallee field for a north or south wind. Winds at 45° angles were simulated by changing
8
1
the horizontal grid parameter from 10 m to 14 m, resulting in a tree belt of width 14 m
2
and an alley width of 84 m. For all model calculations, the alley vegetation was given a
3
height of 0.5 m and a LAI of 1.0, broadly in line with expectations for a growing wheat
4
crop. The distribution of leaf area within the canopy was specified with the shape
5
parameter α (Markkanen et al. 2003), which was set at 3.0, representing a normal curve
6
with maximum LAI at a height of half the vegetation height (0.25 m in this case). Tree
7
height was set at either 2 m, 3 m, or 4 m, and measurement height was set at 3.7 m, 5.4 m
8
or 6.0 m respectively, reflecting experimental procedures. The shape parameter α for the
9
tree belts was set at 5, giving maximum LAI at 0.75 times the tree height. For all tree
10
heights, calculations were performed for tree LAI of 1.0, 2.0 and 3.0, and for geostrophic
11
wind velocities of 3 and 10 m/s. (Note that footprints for geostrophic wind velocities less
12
than 3 m/s could not be calculated due to numerical instabilities in the model.) Surface
13
roughness for the soil was set at 0.01 m for all calculations.
14
Wind direction analysis
15
Wind direction was stratified into 20° classes for both the oil mallee and control fields,
16
for periods associated with each of the different measurement heights in the oil mallee
17
field. The distribution of wind direction was compared between the two fields to
18
determine the impact of the parallel tree belts on wind direction. The standard deviation
19
of wind direction was also assessed in 20° classes for the oil mallee field for each
20
measurement height.
9
1
Average hourly vertical wind speed ( W ) analysis
2
Because of the different vegetation characteristics within the oil mallee field, air
3
temperature over each of the components could also be different, which could lead to
4
consistent patterns of vertical air movement in the field. To identify if this was occurring,
5
W was analysed as a function of horizontal wind direction for each measurement
6
height. The sonic anemometer was not installed perfectly vertically, and so there was a
7
sinusoidal artefact in the graph of W against wind direction. This artefact was removed
8
prior to analysis using a function of the form:
9
10
W
cor
= (( W /√( U 2 + V 2)) - (a sin(wind dir – b))) * √( U 2 + V 2)
(1)
11
12
where W
13
vectors (prior to axis rotation), a = tan Ф, the angle of tilt, and b is the direction of tilt
14
plus 90°. The parameters a and b were estimated from a graph of W /√( U 2 + V 2)
15
against wind direction (see Figure 2). We anticipate that under conditions of persistent
16
vertical air movement due to differences in temperature between the vegetation types,
17
W
cor
cor
is the tilt-corrected vertical wind speed, U and V are the horizontal wind
at the point of measurement in the centre of the alley should be biased towards
18
either positive (upward) or negative values when the wind is from the east or west,
19
blowing along the alleys. Biases in W
20
indicate direct influence of the tree belt on wind patterns.
cor
for winds from the north or south would
10
1
Results
2
Seasonal conditions
3
The traditional winter and spring growing season (May to October) rainfall was 117 mm
4
in 2006, and 251 mm in 2007. The long-term average is 244 mm, so 2007 was close to
5
average, but 2006 was much drier than average. Summer and autumn (November to
6
April) rainfall was 182 mm for 2005/06, 78 mm for 2006/07, and 124 mm for 2007/08.
7
The long-term average for November to April is 101 mm, so the summer and autumn of
8
2005/06 (leading into the first period of measurement) was considerably wetter than
9
average, but the next two summers and autumn periods experienced close-to-average
10
rainfall.
11
Footprint analysis
12
For all modelled scenarios, more than 80% of the total flux signal at the point of
13
measurement was sourced from within 4 tree belts (Table 1). Taller trees with higher LAI
14
resulted in a higher proportion of the signal coming from further than 4 belts, particularly
15
for the higher geostrophic wind velocity, but this was always less than 20% of the total
16
signal. In all calculations for winds at right angles to the tree belts, the presence of tree
17
belts had a noticeable impact on the contribution function (which describes the
18
contribution of the various landscape elements to the total flux measurement),
19
particularly immediately upwind of the belt (Figure 3). Similar results were calculated for
20
winds at 45° to the tree belts (data not shown). In the calculations, tree belts occupied
21
14.3% of the landscape, and their contribution to the total footprint varied from 6.4% (for
22
windy conditions at a measurement height of 3.7 m over 2 m trees with a LAI of 1.0) to
11
1
20.2% (for calm conditions at a measurement height of 6.0 m over 4 m trees with LAI of
2
1.0) (Table 2), and decreased with increasing LAI.
3
Wind direction
4
The presence of tree belts had little impact on wind direction for any of the measurement
5
heights (Figure 4). However, for measurement height of 3.7 m, the standard deviation of
6
wind direction was increased by the tree belts for winds from the north and south,
7
blowing across the belts (Figure 5). Standard deviation of wind direction was not affected
8
by the tree belts for higher measurement heights.
9
Vertical wind speed
10
Vertical wind speed at the point of measurement (between the tree belts) was analysed to
11
determine whether the landscape patterns were having a consistent impact on vertical air
12
movement. W
13
the tree belts and alleys), but was biased towards positive values for wind directions
14
within 45° of east and west (along the alleys) for all measurement heights (Figure 6).
15
These peaks are consistent with persistent patterns of vertical air movement caused by the
16
repeating spatial patterns of vegetation type, and data for winds from these directions
17
were excluded from subsequent analysis. Exclusion of this data had a minor impact on
18
total water and carbon dioxide fluxes (Table 3).
cor
was generally close to zero for winds from the north or south (across
12
1
Comparison of eddy covariance units
2
For the period in March 2007 when both units were installed over short, well-watered
3
grass for a total of 68 hours, regression of hourly-averaged latent energy flux yielded a
4
slope of 1.016, an intercept of 2.53 w/m2, and r2 of 0.98. Values for LE varied between 0
5
and 300 w/m2. Corresponding values for the regression of CO2 flux were 0.994 (slope),
6
0.020 mg/m2/s (intercept), and 0.96 (r2), with a range of -0.4 to +0.5 mg/m2/s.
7
Evapotranspiration
8
During the drier than average growing season of 2006, there was little difference in total
9
evapotranspiration (ET) measured from the two fields (Figure 7). ET from the control
10
field was slightly higher than ET from the oil mallee field for much of the period.
11
However, ET from the oil mallee field started to increase relative to the control field late
12
in the measurement period, coinciding with the period of growth of the oil mallees. Total
13
ET from both fields was greater than rainfall, due to soil water storage from the unusually
14
heavy summer rainfall in 2005/06.
15
Similar results were observed for the April 2007 - October 2007 period in the data set
16
accumulated between April 2007 and May 2008 (Figure 8). During this 6-month period
17
there was little difference in ET between the two fields, but on this occasion, ET was
18
closely aligned with total rainfall for the period.
19
Substantial differences in total ET between the two fields were observed during the
20
summer and autumn period between November 2007 and April 2008 (Figure 8). ET from
21
the control field closely matched rainfall until early in April 2008, but ET was greater
13
1
from the oil mallee field, and total difference for the year based on 2007/08 data was
2
around 30 mm.
3
Carbon balance
4
During the drier than average period between May and October 2006, carbon uptake was
5
initially faster from the control field than from the oil mallee field (Figure 9). However,
6
towards the end of the period of measurement, carbon uptake accelerated from the oil
7
mallee paddock, in line with seasonal patterns of oil mallee growth. During the 2007
8
growing season (Figure 10), carbon uptake was initially greater in the oil mallee paddock,
9
but the maximum rate of assimilation was greater for the control field, so that difference
10
between the two fields was relatively small in October at the end of the growing season.
11
Over the summer of 2007/08, carbon uptake continued from the oil mallee paddock but
12
was much slower from the control paddock, so that over the full year, a net difference of
13
3.2 Mg CO2 was measured.
14
Discussion
15
Eddy covariance over tree belt systems
16
The eddy covariance technique is usually applied to landscapes with relatively uniform
17
topography and vegetation. However, for a ‘belt and alley’ landscape, vegetation is
18
clearly not uniform, and the presence of regular belts of trees could induce complex flow
19
patterns, particularly for wind directions across the belts. For this reason, we examined
20
footprint patterns with the SCADIS tool (Sogachev and Lloyd 2004; Sogachev et al.
21
2005b). According to footprint calculations, the proportion of the flux measured by eddy
14
1
covariance attributable to the tree belts varied depending on the LAI of the tree belts, the
2
measurement height, the tree height, and wind velocity. Oil mallee tree belts in south-
3
western Australia have average LAI of between 2 and 3 (Oliver et al. 2005). For these
4
values, a measurement height of 3.7 m, with a tree height of 2 m (as used for our
5
measurements between May 2006 and July 2006), could result in substantial under-
6
estimation of the tree belt contribution to the total flux (Table 3). However, when the
7
measurement height was increased to 5.4 m (July 2006 to October 2006) or 6.0 m (April
8
2007 to May 2008), the tree belt (LAI 2 or 3) contribution to the calculated footprint
9
varied between 10.2% and 15.6% for winds directly across the tree belts, which was close
10
to the theoretical tree area of 14.3%. Similar results were calculated for winds at 45° to
11
the tree belts, when contribution of the tree belts to the total signal under the same
12
conditions varied between 11.4% and 18.9%. These calculations suggest that in theory,
13
the eddy covariance technique can be applied to measurements from a ‘belt and alley’
14
landscape in order to determine relative contributions from the tree belts and intervening
15
alley vegetation.
16
An analysis of wind direction and its standard deviation found that the regular tree belts
17
in the oil mallee field had little impact on horizontal wind movement, compared with the
18
control field. This further supports the use of the eddy covariance technique for the
19
comparative analysis of carbon and water budgets of a ‘belt and alley’ system. However,
20
given the previously studied effects of tree belts on wind patterns (e.g. Tuzet and Wilson,
21
2007), these impacts should be investigated for each application of eddy covariance to
22
non-uniform vegetation.
15
1
Furthermore, we found evidence of persistent patterns of vertical air movement
2
associated with winds oriented along the tree belts (see Figure 6), providing evidence of
3
advection. However, in this instance the removal of data from potentially advective wind
4
directions had a minimal impact on the total flux estimates. Once again, this should be
5
confirmed for other sites with non-uniform vegetation.
6
Under the rainfall conditions experienced during the experiment, runoff and drainage
7
losses were expected to be small (most likely, zero), and so according to the water
8
balance equation, rainfall should be similar in magnitude to evapotranspiration. This was
9
most clearly observed for data in December 2007 and January 2008 (Figure 3), with
10
increases in cumulative ET matching increases in cumulative rainfall. The close
11
alignment of rainfall and ET gives confidence that the eddy covariance technique is
12
providing a robust estimate of the energy and water balance under the experimental
13
conditions. Carbon flux data also matches closely with patterns of vegetation growth. For
14
these reasons, we are confident in concluding that the eddy covariance technique can
15
successfully be applied to our ‘belt and alley’ landscape. However, similar tests with
16
regard to wind direction and vertical air movement should be performed prior to applying
17
the eddy covariance technique to other areas of non-uniform vegetation.
18
Water balance and salinity
19
The extra water use at the landscape scale measured from the oil mallee field over a full
20
year of measurement was around 30 mm. This difference was generated between
21
December 2007 and May 2008 (Figure 8), with total ET values of 113 and 84 mm for the
22
oil mallee and control fields respectively. Given the very close relationship between the
16
1
two eddy covariance units (where the units differed in their flux calculations by less than
2
2% when positioned over the same vegetation), this represents a significant difference.
3
For comparison, previously published values of extra water use for lucerne in a similar
4
environment varied between 60 and 200 mm (Ward et al. 2001; Latta et al. 2001; Ward et
5
al. 2006). Values quoted in these studies assumed that lucerne was grown over the whole
6
landscape, but in the current study, oil mallees were only planted on about 14% of the
7
landscape. Therefore, the extra water use on the 14% of the landscape occupied by trees
8
was of the order of 210 mm. This water was likely drawn from deep in the soil below the
9
tree belts, and also from significant distances laterally from the tree belt, as observed by
10
Ellis et al. (2005).
11
Averaged over the landscape, the extra water use is likely to help restrict the spread of
12
dryland salinity. Calculations based on LeBuM (Ward 2006) suggest that average annual
13
groundwater recharge for the region could be reduced from 6 mm to less than 2 mm.
14
However, because the spatial distribution of the extra water use is likely to be non-
15
uniform, the actual impact on groundwater recharge is likely to be more complex (Walker
16
et al. 2003), and further research with spatial groundwater models will be necessary to
17
determine the likely outcome.
18
Carter et al. (2005) and Wildy et al. (2004) used sap flow techniques in similar
19
environments to measure oil mallee ET of 2-3 mm/day (based on tree crown area) in
20
December, in the absence of groundwater. Assuming coverage of 14% of the landscape,
21
this equates to 0.3-0.4 mm/day at the landscape scale. In our measurements, the
22
difference in total ET between the oil mallee paddock and the control paddock (i.e. the
17
1
amount of ET attributable to the trees) between December 2007 and February 2008
2
inclusive was 15.2 mm (0.17 mm/day), which is about half that reported by Carter et al.
3
(2005) and Wildy et al. (2004). Even allowing for a possible under-estimation of the tree
4
belts in the total flux footprint as discussed above, our measurements are below the lower
5
end of the range of previous water use estimates. As noted by Crosbie et al. (2008),
6
scaled-up sap flow calculations might over-estimate landscape-scale ET from tree belts
7
by as much as 100%, and our results provide further evidence of this. Nevertheless, the
8
extra water use provided by the oil mallees is likely to assist in restricting the spread of
9
dryland salinity.
10
Carbon balance and sequestration
11
The carbon balance of agricultural systems is influenced by carbon exported from the
12
system, either as grain, hay, or as growth of grazing animals. For this reason, the numbers
13
presented in Figures 9 and 10 do not represent the net carbon gain for the system.
14
Nevertheless, differences in carbon uptake measured during summer and autumn
15
(November to April, when export from the system was minimal) do represent carbon
16
uptake by the oil mallees relative to the control field. Measurements between November
17
2007 and April 2008 indicated that the net uptake of CO2 in the oil mallee field was 1.84
18
Mg CO2/ha, and the corresponding number for the control field was 0.35 Mg CO2/ha.
19
This difference is far greater than the 1-2% difference indicated by the comparison of ET
20
units. Assuming that the difference of 1.49 Mg CO2/ha over the 6-month period is solely
21
attributable to the presence of the oil mallees, this is equivalent to a dry matter (DM) gain
22
of around 0.74 Mg DM/ha at the landscape scale, which will include both above-ground
18
1
and below-ground growth. Wildy and Pate (2002) measured above- and below-ground
2
growth of oil mallee trees growing in belts of around 10 and 3 kg/tree/year respectively,
3
which equated to 1.4 Mg DM/ha/year of total growth at the landscape scale, which is
4
comparable with our measurement of 0.74 Mg DM/ha for the 6-month summer and
5
autumn period.
6
Carbon capture by an agroforestry system will depend on seasonal conditions, as well as
7
the spacing and arrangement of trees within the system. For this reason, it is difficult to
8
compare results from one study with those of another. Nevertheless, the results presented
9
here confirm that the incorporation of belts of trees into a landscape dominated by annual
10
agricultural communities will lead to increased carbon sequestration. Further research
11
will be necessary to determine the impacts on carbon sequestration quantity likely to be
12
associated with spatial arrangement of trees, and this will affect the adoption of ‘belt and
13
alley’ farming systems.
14
Carbon sequestration of 1.5 Mg CO2/ha (as measured for the 2007/08 summer) could be
15
enough to make oil mallee alley farming more attractive in the south-west of Western
16
Australia. With the potential for carbon trading, a price of $20/Mg provides the oil
17
mallees with extra value of $30/ha. Although this on its own is not enough to offset the
18
value of the loss of production from the annual crops displaced by the tree belts, when
19
combined with other tree products (solvent oil, energy, activated charcoal) and
20
environmental benefits (salinity, biodiversity), belts of oil mallees might become a more
21
attractive option than they currently are (Roberts et al. 2008).
19
1
Conclusions
2
The eddy covariance technique can be successfully applied to a belt and alley farming
3
system, although caution must be exercised due to the potential for influences on wind
4
direction and patterns of advective air movement. Measurements of water use indicated
5
that belts of oil mallees occupying 14% of the landscape used an extra 30 mm across the
6
landscape, which is comparable at the landscape scale to water use measured for
7
herbaceous perennial vegetation occupying 100% of the landscape. Whole-of-landscape
8
measurements with eddy covariance suggest that sap flow measurements might lead to
9
overestimation of landscape-scale water use. CO2 measurements indicate that carbon
10
sequestration of 1.5 Mg CO2/ha was achieved during the 2007/08 summer, which could
11
help to increase adoption of oil mallees. Carbon sequestration and ET are likely to vary
12
considerably depending on seasonal conditions, and further research will be necessary to
13
determine actual values over the longer term.
14
Acknowledgements
15
Thanks to Neil Ballard and family for their enthusiastic adoption of oil mallees, and for
16
allowing us to measure the on-farm impacts. Thanks also to Ian Foster of DAFWA for
17
access to their weather station data from the oil mallee paddock. Ray Leuning provided
18
very helpful advice on the analysis of eddy covariance data, and the potential impact of
19
tree belts on advective patterns of air movement. Rob Clement assisted with aspects of
20
eddy covariance data analysis.
20
1
References
2
Baldocchi, D., 2008. TURNER REVIEW No. 15. ‘Breathing’ of the terrestrial biosphere:
3
lessons learned from a global network of carbon dioxide flux measurement systems.
4
Australian Journal of Botany, 56: 1-26.
5
Carter, J., Ward, P.R., White, D.A., Brooksbank, K., Rance, S. and Walker, S. 2004.
6
Water use and growth of oil-mallees with variation in groundwater availability. In:
7
Ridley, A., Feikema, P., Bennett, S., Rogers, M.J., Wilkinson, R. and Hirth, J. (Editors),
8
Salinity Solutions: Working with Science and Society. CRC for Plant-Based Management
9
of Dryland Salinity, Bendigo, Australia.
10
Crosbie, R., Wilson, B., Hughes, J. and McCulloch, C., 2008. The upscaling of
11
transpiration from individual trees to areal transpiration in tree belts. Plant and Soil, 305:
12
25-34.
13
Ellis, T., Hatton, T. and Nuberg, I., 2005. An ecological optimality approach for
14
predicting deep drainage from tree belts of alley farms in water-limited environments.
15
Agricultural Water Management, 75: 92-116.
16
Goulden, M.L., Munger, J.W., Fan, S.M., Daube, B.C. and Wofsy, S.C., 1996.
17
Measurements of carbon sequestration by long-term eddy covariance: Methods and a
18
critical evaluation of accuracy. Global Change Biology, 2: 169-182.
19
Haile, S.G., Nair, P.K.R. and Nair, V.D., 2008. Carbon storage of different soil-size
20
fractions in Florida silvopastoral systems. Journal of Environmental Quality, 37: 1789-
21
1797.
21
1
Latta, R.A., Blacklow, L.J. and Cocks, P.S., 2001. Comparative soil water, pasture
2
production, and crop yields in phase farming systems with lucerne and annual pasture in
3
Western Australia. Australian Journal of Agricultural Research, 52: 295-303.
4
Lefroy, E.C., Flugge, F., Avery, A. and Hume, I., 2004. Potential of current perennial
5
plant-based farming systems to deliver salinity management outcomes and improve
6
prospects for native biodiversity: a review. In: Ridley, A., Feikema, P., Bennett, S.,
7
Rogers, M.J., Wilkinson, R. and Hirth, J. (Editors), Salinity Solutions: Working with
8
Science and Society. CRC for Plant-Based Management of Dryland Salinity, Bendigo,
9
Australia.
10
Loescher, H.W., Ocheltree, T., Tanner, B., Swiatek, E., Dano, B., Wong, J., Zimmerman,
11
G., Campbell, J., Stock, C., Jacobsen, L., Shiga, Y., Kollas, J., Liburdy, J., Law, B.E.,
12
2005. Comparison of temperature and wind statistics in contrasting environments among
13
different sonic anemometer-thermometers. Agricultural and Forest Meteorology, 133:
14
119-139.
15
Mao, S.L., Leclerc, M.Y. and Michaelides, E.E., 2008. Passive scalar flux footprint
16
analysis over horizontally inhomogeneous plant canopy using large-eddy simulation.
17
Atmospheric Environment, 42: 5446-5458.
18
Markkanen, T., Rannik, U., Marcolla, B., Cescatti, A. and Vesala, T., 2003. Footprints
19
and fetches for fluxes over forest canopies with varying structure and density. Boundary-
20
Layer Meteorology, 106: 437-459.
22
1
McFarlane, D.J., George, R.J. and Caccetta, P.A., 2004. The extent and potential area of
2
salt-affected land in Western Australia estimated using remote sensing and digital terrain
3
models, 1st National Salinity Engineering Conference, Perth, Western Australia.
4
Nair, P.K.R., Kumar, B.M. and Nair, V.D., 2009. Agroforestry as a strategy for carbon
5
sequestration. Journal of Plant Nutrition and Soil Science-Zeitschrift Fur
6
Pflanzenernahrung Und Bodenkunde, 172: 10-23.
7
Oliver, Y.M., Lefroy, E.C., Stirzaker, R. and Davies, C.L., 2005. Deep-drainage control
8
and yield: the trade-off between trees and crops in agroforestry systems in the medium to
9
low rainfall areas of Australia. Australian Journal of Agricultural Research, 56: 1011-
10
1026.
11
Ong, C.K., Wilson, J., Deans, J.D., Mulayta, J., Raussen, T. and Wajja-Musukwe, N.
12
2002. Tree–crop interactions: manipulation of water use and root function. Agricultural
13
Water Management 53(1-3): 171-186.
14
Parrotta, J.A., 1999. Productivity, nutrient cycling, and succession in single- and mixed-
15
species plantations of Casuarina equisetifolia, Eucalyptus robusta, and Leucaena
16
leucocephala in Puerto Rico. Forest Ecology and Management, 124: 45-77.
17
Roberts, A.M., Helmers, M.J. and Fillery, I.R.P., 2009. The adoptability of perennial-
18
based farming systems for hydrologic and salinity control in dryland farming systems in
19
Australia and the United States of America. Crop and Pasture Science, 60: 83-99.
23
1
Sogachev, A., Leclerc, M.Y., Karipot, A., Zhang, G. and Vesala, T., 2005a. Effect of
2
clearcuts on footprints and flux measurements above a forest canopy. Agricultural and
3
Forest Meteorology, 133: 182-196.
4
Sogachev, A. and Lloyd, J., 2004. Using a one-and-a-half order closure model of the
5
atmospheric boundary layer for surface flux footprint estimation. Boundary-Layer
6
Meteorology, 112: 467-502.
7
Sogachev, A., Panferov, O., Gravenhorst, G. and Vesala, T., 2005b. Numerical analysis
8
of flux footprints for different landscapes. Theoretical and Applied Climatology, 80: 169-
9
185.
10
Stirzaker, R.J., Cook, F.J. and Knight, J.H., 1999. Where to plant trees on cropping land
11
for control of dryland salinity: some approximate solutions. Agricultural Water
12
Management, 39: 115-133.
13
Stirzaker, R.J., Lefroy, E.C. and Ellis, T.W., 2002. An index for quantifying the trade-off
14
between drainage and productivity in tree–crop mixtures. Agricultural Water
15
Management, 53: 187-199.
16
Takimoto, A., Nair, P.K.R. and Nair, V.D., 2008. Carbon stock and sequestration
17
potential of traditional and improved agroforestry systems in the West African Sahel.
18
Agriculture Ecosystems & Environment, 125: 159-166.
19
Takimoto, A., Nair, V. and Nair, P., 2009. Contribution of trees to soil carbon
20
sequestration under agroforestry systems in the West African Sahel. Agroforestry
21
Systems, 76: 11-25.
24
1
Tuzet, A. and Wilson, J.D., 2007. Measured winds about a thick hedge. Agricultural and
2
Forest Meteorology, 145: 195-205.
3
Walker, G., Bartel, B., Bourne, J., Hamilton, A. and Young, D., 2003. Low rainfall alley
4
farming: potential for reducing regional salinity in the Murray Basin. Natural Resource
5
Management, 6: 13-23.
6
Ward, P.R., 2006. Predicting the impact of perennial phases on average leakage from
7
farming systems in south-western Australia. Australian Journal of Agricultural Research,
8
57: 269-280.
9
Ward, P.R., Dunin, F.X. and Micin, S.F., 2001. Water balance of annual and perennial
10
pastures on a duplex soil in a Mediterranean environment. Australian Journal of
11
Agricultural Research, 52: 203-209.
12
Ward, P.R., Micin, S.F. and Dunin, F.X., 2006. Using soil, climate, and agronomy to
13
predict soil water use by lucerne compared with soil water use by annual crops or
14
pastures. Australian Journal of Agricultural Research, 57: 347-354.
15
Wildy, D., Pate, J. and Bartle, J., 2004. Budgets of Water Use by Eucalyptus kochii Tree
16
Belts in the Semi-Arid Wheatbelt of Western Australia. Plant and Soil, 262: 129-149.
17
Wildy, D.T. and Pate, J.S., 2002. Quantifying Above- and Below-ground Growth
18
Responses of the Western Australian Oil Mallee, Eucalyptus kochii subsp. plenissima, to
19
Contrasting Decapitation Regimes. Annals of Botany, 90: 185-197.
25
1
Wu, H.W., Fu, Q., Giles, R. and Bartle, J., 2007. Production of mallee biomass in
2
Western Australia: Energy balance analysis, International Conference on Bioenergy
3
Outlook. American Chemical Society, Singapore, SINGAPORE, pp. 190-198.
4
Zhang, G., Thomas, C., Leclerc, M.Y., Karipot, A., Gholz, H.L., Binford, M., Foken, T.,
5
2007. On the effect of clearcuts on turbulence structure above a forest canopy.
6
Theoretical and Applied Climatology, 88: 133-137.
26
1
Table 1. Percentage of footprint signal sourced from within 4 tree belts (300 m for N or S
2
winds, 420 m for NE, NW, SE, or SW winds) of the point of measurement.
Wind
Tree
Sensor
Direction
Height
Height
(m)
(m)
N, S
2
(across
Geostrophic wind 3 m/s
Geostrophic wind 10 m/s
LAI
LAI
LAI
LAI
LAI
LAI
1.0
2.0
3.0
1.0
2.0
3.0
3.7
96
94
93
97
na
na
3
5.4
93
90
89
93
88
87
4
6.0
92
89
88
91
85
83
NE, NW,
2
3.7
97
96
95
96
na
na
SE, SW
3
5.4
95
93
92
94
92
91
4
6.0
94
92
91
94
92
91
tree belts)
(45° to
tree belts)
27
1
Table 2. Percentage of footprint attributable to trees where trees were planted in belts of
2
10 m width with 60 between belts (i.e. total tree area = 14.3%).
Wind
Tree
Sensor
Direction
Height
Height
(m)
(m)
2
Geostrophic wind 3 m/s
Geostrophic wind 10 m/s
LAI
LAI
LAI
LAI
LAI
LAI
1.0
2.0
3.0
1.0
2.0
3.0
3.7
14.8
13.0
12.6
6.4
na
na
3
5.4
18.6
15.2
14.1
11.5
10.9
10.2
4
6.0
20.2
16.6
15.2
14.0
12.7
12.2
NE, NW,
2
3.7
12.9
11.6
11.4
12.7
na
na
SE, SW
3
5.4
17.4
14.6
13.6
17.2
15.1
14.5
4
6.0
19.3
16.0
15.0
18.9
16.7
16.1
N, S
28
1
Table 3. Total water and carbon dioxide fluxes for May to November 2006, and May
2
2007 to April 2008, before and after removal of data associated with winds blowing along
3
the direction of the tree belts.
4
May to November 2006
April 2007 to May 2008
All wind
E and W winds
All wind
E and W winds
directions
excluded
directions
excluded
H2O (mm)
166
161
414
414
CO2 (t/ha)
-3.6
-3.8
-15.0
-15.0
29
1
Captions for Figures
2
Figure 1. Layout of the oil mallee field.
3
Figure 2. An example of the sinusoidal response of W /√( U 2 + V 2) when plotted
4
against wind direction, for the oil mallee field with measurement height 5.4m. In this
5
instance parameters a and b (equation 1) were evaluated as 0.06 and 130 respectively,
6
corresponding with a sensor tilt of 3.4° (= tan-1 0.06) towards the north-east (40°).
7
Figure 3. Calculated footprint contribution functions for north or south winds for tree
8
heights of 2 m ((a) and (b)), 3 m ((c) and (d)), and 4 m ((e) and (f)), for geostrophic wind
9
speeds of 3 m/s ((a), (c) and (e)) and 10 m/s ((b), (d) and (f)). The maximum value for the
10
contribution function in (b) is 0.047 m-1.
11
Figure 4. Frequency of wind direction (in 20° classes) for the oil mallee and control fields
12
for periods when measurement height in the oil mallee field was 3.7 m (a), 5.4 m (b) and
13
6.0 m (c).
14
Figure 5. Average standard deviation of wind direction (in 20° classes) for the oil mallee
15
and control fields for periods when measurement height in the oil mallee field was 3.7 m
16
(a), 5.4 m (b) and 6.0 m (c).
17
Figure 6. Vertical wind speed corrected for tilt angle for measurements taken at heights of
18
3.7 m (a), 5.4 m (b) and 6.0 m (c).
30
1
Figure 7. Cumulative evapotranspiration (actual and potential), and cumulative rainfall
2
from the oil mallee field and the control field for the period May 25 2006 to October 25
3
2006.
4
Figure 8. Cumulative evapotranspiration (actual and potential), and cumulative rainfall
5
from the oil mallee field and the control field for the period April 4 2007 to May 8 2008.
6
Figure 9. Cumulative net CO2 assimilation for the oil mallee field and the control field for
7
the period May 25 2006 to October 25 2006.
8
Figure 10. Cumulative net CO2 assimilation for the oil mallee field and the control field
9
for the period April 4 2007 to May 8 2008.
1
1
Figure1.
2
N
Oil mallees
EC location May 06 – Oct 06
EC location Apr 07 – May 08
Field boundary
250 m
1
1
Figure 2.
0.10
0.05
0.00
Data points
Fitted sine curve
-0.05
-0.10
0
90
180
Wind direction (degrees)
270
360
2
Figure 3
(a)
(b)
Veg Ht
Tree LAI 1.0
Tree LAI 2.0
Tree LAI 3.0
15
0.02
10
0.01
5
0.00
0
(c)
(d)
15
0.02
10
0.01
5
0.00
0
(e)
(f)
15
0.02
10
0.01
5
0.00
0
-400
-300
-200
-100
0
-400
-300
Distance from sensor (m)
-200
-100
0
Contribution function (m-1)
Vegetation height (m)
1
3
1
Figure 4
2
(a)
Oil mallee field
Control field
0.10
0.05
0.00
Frequency
(b)
0.10
0.05
0.00
(c)
0.10
0.05
0.00
0
90
180
270
Wind direction (degrees)
360
4
1
Figure 5
2
Average standard deviation of wind direction (degrees)
(a)
50
Oil mallee field
Control field
40
30
20
(b)
50
40
30
20
(c)
50
40
30
20
0
90
180
270
Wind direction (degrees)
360
5
1
Figure 6
2
(a)
0.1
0.0
-0.1
(m/s)
(b)
0.1
0.0
-0.1
(c)
0.1
0.0
-0.1
0
90
180
270
Wind direction (degrees)
360
1
1
Figure 7
2
Cumulative ET or rainfall (mm)
250
200
150
Oil mallee
Control
Potential ET
Rainfall
100
50
0
1 May 06
1 Jul 06
1 Sep 06
1 Nov 06
2
1
Figure 8
2
Cumulative ET or rainfall (mm)
500
400
300
200
Oil mallee
Control
Potential ET
Rainfall
100
0
1 Apr 07
1 Jul 07
1 Oct 07
1 Jan 08
1 Apr 08
3
1
Figure 9
Cumulative flux (Mg CO2/ha)
2
0
-1
-2
Oil mallee
Control
-3
-4
1 May 06
1 Jul 06
1 Sep 06
1 Nov 06
4
1
Figure 10
Cumulative flux (Mg CO2/ha)
2
0
Oil mallee
Control
-4
-8
-12
-16
1 Apr 07
1 Jul 07
1 Oct 07
1 Jan 08
1 Apr 08