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RESEARCH ARTICLE
Remarkable agrivoltaic influence on soil
moisture, micrometeorology and water-use
efficiency
Elnaz Hassanpour AdehID*, John S. Selker, Chad W. Higgins
Department of Biological and Ecological Engineering, Oregon State University, Corvallis, Oregon, United
States of America
*hassanpe@oregonstate.edu
Abstract
Power demands are set to increase by two-fold within the current century and a high fraction
of that demand should be met by carbon free sources. Among the renewable energies,
solar energy is among the fastest growing; therefore, a comprehensive and accurate design
methodology for solar systems and how they interact with the local environment is vital. This
paper addresses the environmental effects of solar panels on an unirrigated pasture that
often experiences water stress. Changes to the microclimatology, soil moisture, water
usage, and biomass productivity due to the presence of solar panels were quantified. The
goal of this study was to show that the impacts of these factors should be considered in
designing the solar farms to take advantage of potential net gains in agricultural and power
production. Microclimatological stations were placed in the Rabbit Hills agrivoltaic solar
arrays, located in Oregon State campus, two years after the solar array was installed. Soil
moisture was quantified using neutron probe readings. Significant differences in mean air
temperature, relative humidity, wind speed, wind direction, and soil moisture were observed.
Areas under PV solar panels maintained higher soil moisture throughout the period of obser-
vation. A significant increase in late season biomass was also observed for areas under the
PV panels (90% more biomass), and areas under PV panels were significantly more water
efficient (328% more efficient).
1 Introduction
Global energy demand will be doubled by mid-century due to population and economic
growth [1,2]. Renewable and environmental-friendly energies will play a vital role to meet this
demand.
Among all renewable energies, solar power is the most abundant and available source [3].
Solar power is also becoming more affordable. The cost of solar panels has fallen by 10% per
year for the past thirty years, while production has risen by 30% per year. If costs continue to
be reduced based on this historic rate, solar energy will be less expensive than coal by 2020[4].
The impact of wide-spread solar installations is an area of increasing interest. Regional
PLOS ONE | https://doi.org/10.1371/journal.pone.0203256 November 1, 2018 1 / 15
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OPEN ACCESS
Citation: Hassanpour Adeh E, Selker JS, Higgins
CW (2018) Remarkable agrivoltaic influence on soil
moisture, micrometeorology and water-use
efficiency. PLoS ONE 13(11): e0203256. https://
doi.org/10.1371/journal.pone.0203256
Editor: Mauro Villarini, Universita degli Studi della
Tuscia, ITALY
Received: January 18, 2017
Accepted: August 18, 2018
Published: November 1, 2018
Copyright: ©2018 Hassanpour Adeh et al. This is
an open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data are available
from the Oregon State University library (ir.library.
oregonstate.edu) with DOI: 10.7267/N9W37T8D.
Funding: This material is based upon work that is
supported by the National Institute of Food and
Agriculture, U.S. Department of Agriculture, under
award number OREZ-FERM-852-E, and by National
Science Foundation award number EAR –
1740082. The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
climatology may be influenced by large scale solar installations, but simulations have provided
conflicting results: 3–4˚C increase in air temperature over solar panels compared to wildlands
at night [5], 0.1–0.5˚C decrease in air temperature [6], 26˚C increase in the shaded roof top
temperature compared with unshaded roof top [7], 1–2.5˚C increase in regional and global
temperatures in urban area [8] and a 5.2˚C increase in air temperature under solar panels [9].
Solar installations can occupy large land areas and sometimes compete with agriculture for
the land resource [10]. Agrivoltaic systems are created when solar and agricultural systems are
co-located for mutual benefit. The formal introduction of agrivoltaic systems is credited to
Dupraz in 2011 [11]. Land demand for energy production decreases profoundly when agrivol-
taics are used [10]. Not all agricultural crops are suitable, but plants with less root density and
a high net photosynthetic rate are ideal candidates [11]. Agrivoltaic systems have been shown
to increase land productivity by 60–70% [12], and increase the value of energy production sys-
tem by 30% [13]. Very limited experimental research was found on the impacts of a solar
arrays on agricultural production. Marrou et al. [14] measured soil water potential and soil
water gradient (difference between uptake and drainage) in cucumber and lettuce and revealed
lower soil water potential under the panels. This water potential led to an increase in harvested
final fresh weight. Another experiment by Marrou et el. [15] found that plants cover soil faster
under the shade of solar panels. An experimental study by Dupraz et al. demonstrated that
summer crops benefited of solar shade more than winter crops such as pea and wheat crops
[16]. Co-locating agave plant below solar panels increased yield per m
3
of water used in the
San Bernardino County in California [17]. Non-beneficial effects have also been observed in
Welch onion fields where, photovoltaics reduced the fresh and dry matter harvest weight [18].
In this paper, a field study was performed to measure the effects of a six-acre agrivoltaic
solar farm on the microclimatology, soil moisture and pasture production. The experimental
setup included microclimatological and soil moisture measurements from May to August
2015 in Rabbit Hills agrivoltaic solar arrays, located on the Oregon State University campus.
The field data for this study is accessible through Oregon State library system [19].
2 Material and methods
The field study was performed on a six acre agrivoltaic solar farm and sheep pasture near the
Oregon State University Campus (Corvallis, Oregon, US.). The PhotoVoltaic Panels (PVPs)
have been arranged in east–west orientated strips, 1.65 m wide and inclined southward with
a tilt angle of 18
o
. PVPs have been held at 1.1 meters above ground (at lowest point) and
the distance between panels is 6 meters as shown in Fig 1) e. The whole solar array system
has a capacity of 1435 kilowatts (http://fa.oregonstate.edu/sustainability/ground-mounted-
photovoltaic-arrays). As shown in Fig 1, the data were collected from localized zones (descri-
bed hereafter) including areas below solar panels and a control area outside the agrivoltaic sys-
tem. The pasture below the solar panels and the control areas were in the same paddock that
was actively grazed by sheep. Exclusionary plots, to eliminate grazing pressure, were main-
tained with fencing. The total size of the fenced areas was limited by agricultural activities. The
pasture was not irrigated, and typically experiences water stress mid-summer. The soil classifi-
cation for >70% of the pasture area (control and agrivoltaic system) is Woodburn Silt clay
[20]. The control and treatment plots were located within Woodburn Silt clay classification
areas. The intent of the field measurements was to minimize uncontrollable differences
between the treatments and control (e.g. solar forcing, soil types) and minimize impact on
agricultural activities. Thus, the distance between the treatment site and the control site was
kept minimum. The observations within the treatment site were further divided into three
sub-treatments (Fig 2): (1) Sky Fully Open area between panels (SFO), (2) Solar Partially Open
Environmental effects of solar panel on agricultural fields
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Competing interests: The authors have declared
that no competing interests exist.
Fig 1. a) Aerial photo of 35
th
Street agrivoltaic solar array, Oregon State University Corvallis campus (this photo is taken in winter and shadow pattern
is different from the measurements which held in summer) Copyright: Oregon State University, b) Solar panel set up, c) Control area set up, d) Shade
zones in solar panel, e) Schematic drawing of shade zones (H is object height and L is shadow length).
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Environmental effects of solar panel on agricultural fields
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between panels (SPO) and (3) Solar Fully Covered area under panels (SFC). SFO areas are
between the edges of installed PV panels and experienced full sun. Shadow length calculation
also confirms no shade covers the SFO zone [21]. SPO areas are in the penumbra and experi-
enced episodic shade. SFC areas are directly beneath the PV panels and experienced full shade.
Data from these sub-treatments were compared to the data collected from the control area out-
side the agrivoltaic array, where each measurement was replicated.
Shadow length (L) is calculated [20]based on the sun latitude, solar panel height, day and
time of the year the and it changes from 1.1 meters to 1.4 meters for May, June, July and
August of 2015 which makes the SFO no shadow zone.Data were collected continuously in all
areas from May-August 2015. Air temperature, relative humidity, wind speed and wind direc-
tion measurements were collected on 1 minute intervals. Soil moisture profiles were collected
three times each week, and biomass samples were collected at the end of the observation
period. Details associated with each set of measurements are explained in the following sub-
sections.
2.1 Microclimatological measurements
Two atmospheric profiling stations were installed 70 meters apart: one in the control area and
one near the center of the solar panel area. Micrometeorological variables were collected at
four levels (0.5, 1.2, 2.0 and 2.7 m aboveground) in 1 minute intervals. The gathered variables
were (1) air temperature (VP-3 Decagon Devices), (2) wind speed and directions (DS-2 Deca-
gon Devices), (3) relative humidity (VP-3 Decagon Devices) and (4) net radiation (PYR
Fig 2. Plan view of experimental setup in solar array area showing locations of towers and neutron probe access
tubes for: Solar Fully Covered (SFC), Solar partially open (SPO), Sky Fully Open (SFO), solar measurements are
almost 70 meters apart from control area.
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Decagon Devices). Data were logged on EM50 data loggers (Decagon Devices). Temperature
and humidity devices were calibrated in a chamber, and wind sensors were calibrated in a
wind tunnel prior to installation. A Kolmogorov Smirnov test was used to detect differences in
distributions of temperature, humidity, wind speed, wind direction, and down welling radia-
tion between the solar array area and the control area. A two tailed t-test was used to detect dif-
ferences in the mean temperature, humidity, wind speed, wind direction, and down welling
radiation between the solar array area and the control area and standard deviation results was
measured to quantify the amount of dispersion of a set of data values.
2.2 Soil moisture measurement
The soil moisture was obtained using a neutron probe device (503 DR hydro-probe Campbell
Pacific Nuclear International Inc. BoartLongyear Corporation (CPN), Concord, California,
USA). These data were gathered at six depths for each sampling location (0.1 m to 0.6 m in 0.1
m intervals). Fig 2 shows a plan view where nine neutron probe access tubes for soil moisture
measurements were installed in the solar area. Three access tubes were installed in each sub-
treatment: SFO, SPO, and SFC respectively. Three access tubes were also installed in the con-
trol area. Neutron Probe readings were taken approximately every three days. A standard
count was taken prior to sampling each day to calibrate data readings. Three neutron counts
were averaged for each individual measurement (a single depth in a single tube). This count
was normalized by the standard count, and the normalized count was calibrated to soil mois-
ture. Within each sub-treatment, data at the same depths are averaged to determine the soil
moisture profile and error-bars. The result is a soil moisture profile with measurements at 0.1,
0.2, 0.3, 0.4, 0.5, and 0.6 m for each sub-treatment and the control every three days. Neutron
probe readings at the 0.1m depth for all sub-treatments and the control were adjusted to
account for possible neutron losses to the atmosphere [22]. Two-way ANOVA was used to test
the independence of the soil moisture measurements with respect to zoning (the control, SFO,
SPO, and SFC) and depth.
2.3 Biomass measurements
Above-ground biomass was collected on the 28
th
of August. Six 1m by 1m quadrants were col-
lected from within the fenced areas for each sub-treatment and the control. Harvested biomass
was dried for 48 hours in a 105
o
C oven and weighed. The Daubenmire method [11] was used
to study grass species diversity at the end of July. Six transects in the control and one transect
in the solar array were performed. For each transect, a random number was drawn (from
1–10) to determine the final location of each 1m x1m quadrant. Six quadrants were collected
in each transect resulting in a total of 42 samples. In each quadrant, the coverage, by species,
was determined visually.
3 Results and discussions
3.1 Micrometeorological variables
Using a Kolmogorov Smirnov test, a two tailed t-test, standard deviation and William Watson
test[23] for wind direction showed subtle but statistically significant differences. Significant
differences in mean temperature were found in readings taken closest to the PV panel surfaces
at the 1.2 m and 2.0 m elevations. No significant differences were observed at the lowest (0.5
m) or highest (2.7 m) elevations. Note that the magnitude of these mean temperature differ-
ences are smaller than those reported from simulation studies [5–9]. Significant differences in
mean relative humidity and wind speed were found for all measurement heights. Solar
Environmental effects of solar panel on agricultural fields
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radiation below the solar panel installation height was significantly reduced (as expected) and
the incoming solar radiation measured at a height above the solar panels was found to be sta-
tistically significant (unexpected) but the difference relatively small. The distribution of wind
direction was significantly altered at all heights, and the mean wind speed was significantly dif-
ferent at all heights. A summary of the p-values from all statistical tests is shown in Table 1.
Standard deviation values were big due to diurnal changes of micro climate variables during
the day.
Wind direction data at 2.7 m above ground level is shown in Fig 3 to illustrate the alter-
ations in the wind direction. For the sake of brevity, only one height is presented in this manu-
script, but all heights are shown in Supporting Information (Figure A in S1 Appendix). Fig 3
shows a histogram of incident wind direction plotted as a function of direction. Longer spokes
indicate that that particular direction is more probable. Each spoke is divided and colored
according to the strength of the wind (wind speed). For example, a long blue spoke would indi-
cate that light winds from that direction are common. We can conclude from Fig 3 that the
wind direction in the control area is distributed among many incident angles, while the wind
direction within the treatment is oriented predominantly from the south. That is, the wind
direction within the treatment area reorients with solar panels such that the wind is from
south to north. The panels do not act as ‘canyons’ and orient the wind along their rows (a com-
mon occurrence in urban flows for example)[24]. Rather, the wind is reoriented perpendicular
to the solar array’s rows. The authors believe that the local increase in temperature near the
solar panel surface results in a buoyant force that causes local anabatic flow up the panel sur-
faces. Each anabatic flow on each PV row has a vector component perpendicular to the solar
panel row orientation, and the entire solar farm acts like a ‘Fresnel slope’ that reorients the
flow. The total buoyant force is enough to accelerate the flow directionally, and contributes the
increase in wind speed above the panels. Flow acceleration around a bluff body (PV panel)
also contributes to increased wind speed above the solar panels. Increased drag due to the
Table 1. Mean/Std and p-values from solar panel and control area Two-sample Kolmogorov-Smirnov, t tests and William Watson test.
Elevation (m) 0.5 1.2 2.0 2.7
Temperature
(˚C)
Mean/Std (solar panel area) 18.34/7.87 18.03/8.06 18.30/7.39 18.37/7.65
Mean/Std (control area) 18.27/7.85 18.32/8.31 18.36/7.47 18.11/7.64
p-values (KS test) 0.9964 0.9964 1.0000 1.0000
p-values (t test) 0.1527 0.0000 0.0000 0.5996
Relative humidity
(%)
Mean/Std (solar panel area) 65.62/0.226 64.17/0.252 64.29/0.209 64.92/0.230
Mean/Std (control area) 66.23/0.234 66.38/0.242 64.89/0.222 65.37/0.223
p-values (KS test) 0.0004 0.3611 0.7014 0.6703
p-values (t test) 0.0000 0.0000 0.0000 0.0118
Wind speed
(m/s)
Mean/Std (solar panel area) 0.5471/0.506 0.4880/0.427 1.3777/1.083 1.0889/0.909
Mean/Std (control area) 0.8752/0.665 0.6384/0.520 1.1313/0.859 0.9726/0.757
p-values (KS test) 0.9579 1.0000 0.8497 0.9964
p-values (t test) 0.0000 0.0000 0.0000 0.0000
Solar radiation (W/m2) Mean/Std (solar panel area) - 59.53/96.65 - 275.72/322.59
Mean/Std (control area) - 328.26/407.93 - 271.58/323.34
p-values (KS test) - 0.0099 - 0.9597
p-values (t test) - 0.0000 - 0.0054
Wind direction
(˚)
Mean/Std (solar panel area) 196.29/107.71 220.96/102.32 211.83/101.68 206.11/96.65
Mean/Std (control area) 210.54/102.29 196.82/121.16 211.87/95.91 182.13/115.97
p-values (WW test) 0.0000 0.0000 0.0000 0.0000
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‘solar canopy’ is likely the cause of the reduced speed below the solar panels. Note that the
most common wind speeds are weak (<2m/s), and it is unclear if this wind direction shift
would be a robust result for windy locations. Higher wind speeds are also observed to reorient
in Fig 3; however, the number of occurrences are limited.
3.2 Soil moisture data comparisons
The horizontal axis shows the Day of Year (DoY) of the data collection in 2015 and vertical
axis is the volumetric soil moisture in vol/vol. Independence was determined with a p-value of
less than 0.01 for all depths and zones by two-way ANOVA test. The soil moisture is near satu-
ration for all depths and all treatments at the start of observation. That is, all areas had nearly
identical initial soil moistures. The differing rates of soil water depletion in the three sub-treat-
ments and the control led to dramatic differences in late season soil moisture.
The soil moisture in the SFO area is depleted more rapidly than the SPO, SFC or control
areas. This result is intriguing since the SFO area and the control experience similar incident
solar radiation. Thus, the SFO must have a different energetic balance despite similar exposure
to direct solar energy. We hypothesize that this difference in rate of soil moisture loss is a result
of the longwave radiation transfer. The SFO will experience incident long wave radiation from
the adjacent PV panels. These PV panels would also reduce the sky view factor of the SFO
area. In contrast, the sky view in the control area is unobstructed. Thus, we infer that the total
net long wave and net shortwave radiation both play an important role in the energetics and
evaporation in the SFO area. The complete long and short wave radiation budgets within an
agrivoltaic system will be explored in future study.
Time series of the soil moisture at 0.2 m, 0.4 m and 0.6 m are presented in Fig 4 in subpan-
els a-c. Time series of soil moisture at 0.1 m, 0.3 m and 0.5 m can be found in Supporting
Information (Figure A in S2 Appendix). Soil moisture is most persistent in the SFC area and
remains available for a larger portion of the growing season. The soil moisture at 0.6 m depth
remained close to saturation (0.3 vol/vol) for the entire season which implies that water is
available at the bottom of the root zone over the period of observation Fig 4C. Overall the SFC
area remained wetter than all other areas, including the control. This water availability is in
stark contrast to the SFO area which was near saturation at the start of observation (~0.3 vol/
vol) and depleted to ~0.2 vol/vol at the end of the season. This stark contrast in the moisture
availability between the SFO and SFC creates an undesirable variability across the field and
hints that shade uniformity may be an important consideration for the design of future agri-
voltaic systems. The SPO area dries at a rate slower than the SFO but faster than the SFC and
the control.
In other words, for most times and soil depths, the SFC had that highest soil moisture fol-
lowed by the SPO, control and SFO respectively. It should be noted that the mean soil moisture
across the SPO, SFO and SFC regions is similar to the control. But, the solar panels increase
the local heterogeneity of soil water conditions, which results in some areas (SFC) having
more persistent stores of soil water throughout the growing season.
The soil profiles at the beginning and end of the observation period are shown in Fig 5 All
areas were near saturation for all depths initially. By the end of the observation period, the soil
moisture in the SFC zone was nearly twice the SFO. These measurements are separated by less
than two meters spatially. All measured soil moisture profiles are available in Supporting
Information (Figure A in S3 Appendix).
Fig 3. Wind rose plots for control (upper) and solar areas (lower) for May-August average winddirections. The data are
for elevation 2.7 m.
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Fig 4. Soil moisture time series (a) 0.2m, (b) 0.4m and (c) 0.6m. For more information: there was 40 mm precipitation
over the observation period, i.e. May-Aug 2015.
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3.3 Vegetation
Eight grass types were identified in the control pasture and five were identified in the solar
farm area. A summary of the results is presented in Table 2. The most common species in the
solar panel area was Alopecurus, a long-lived perennial that thrives in moist conditions. Alope-
curus provides a “succulent and palatable forage” [25]. The most prevalent grass type in con-
trol area is Hordeum that has spikelet clusters that can enter nostrils and ear canals in
mammals. Three types of grasses Calamagrostis, Cirsium and Dactylis were observed only in
the control area. These grasses are only favored by sheep and cattle in the early stage of the
grass before spine develops [26]. The causal factor for the diversity change between control
and treatment requires further investigation.
The harvested dry biomass at the end of the observation period is shown in Fig 6 Results
show 126% more dry biomass in the SFC zone relative to the SFO zone and 90% more dry bio-
mass in the SFC zone relative to the control. Although the sample size is small, difference
between the SFC and the control were found to be significant, (p-value = 0.007). In addition,
the difference between the SFC and the SFO were found to be significant, (p-value = 0.007).
3.4 Water usage
Water usage was calculated based on difference in depth averaged soil moisture between the
beginning (Fig 5(A) and end (Fig 5(B)) of the observation period. Averages are calculated by
integrating soil moisture over soil depth from 10cm to 60cm. Water Use Efficiency (WUE) is
then calculated as the biomass produced per unit of water used. Water use efficiencies in kg
biomass/m
3
of water against the biomass weight in control and SFO and SFC treatments are
presented in Fig 7 (WUE SFCWUE Control area
WUE Control area ). The higher producing SFC treatment was also signifi-
cantly more water efficient (328%).
The seasonal climate pattern at the site produces an initially saturated pasture and a a dry
growing season. Initial water stores are depleted, through evapotranspiration (ET), and water
scarcity occurs in the control and SFO areas. The shaded treatments (SFC and SPO) experi-
ence lower potential evapotranspiration (PET) due to decreased solar radiation throughout the
observation period which resulted in a slower dry-down of the stored soil water. The decreased
rate of dry-down in the SFC and SPO areas left soil water stores available throughout the
observation period and allowed pasture grasses in the SFC and SPO to accumulate a signifi-
cantly greater biomass. The reduced PET in the SFC and SPO treatments also contributed to
Fig 5. Selected normalized soil moisture profiles from data sampling to show the change in soil moisture through growing season, (a)
May 06–2015 and (b) August 27–2015.
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Table 2. The results of biomass monitoring for different grass types in solar and control area.
Grass scientific name (common name) Solar area (%) Control area (%)
Hordeum (Foxtail barely) 10 25
Agrostis (Redtop bentgrass) 30 20
Alopecurus (Meadow foxtail) 50 7
Schedonorus (Tall rye grass) 5 9
Bromus (Foxtailbrome) 5 22
Calamagrostis (Reed grass) 0 6
Cirsium (Thistle) 0 10.5
Dactylis (Orchard grass) 0 0.5
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Fig 6. Dry biomass comparison in three places Solar Fully Covered (SFC), Sky Fully Open (SFO) and control area.
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Fig 7. Biomass productivity in kg/ m
3
of water.
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an increase in water use efficiency of the pasture grasses. That is, a ‘water limited’ area, in a
Budyiko [27] sense, could be considered as an area of ‘solar excess.’ By harvesting this solar
excess with solar panels, PET is reduced. Taken to an extreme it is possible to shift the aridity
such that the shaded area becomes energy limited. Thus there must exist a shading level, for a
water limited area, where PET and AET would be in balance. We would not expect a similar
outcome in ‘energy limited’ areas (Budyko sense) as observed by Armstrong et al. [8]. In this
case, there is no solar excess and the PET is already equal to the AET. If solar arrays were
placed above growing plants in ‘energy limited’ conditions we would expect that the total bio-
mass production would decrease, consistent with the findings of Armstrong et al. [16].
4 Conclusion
Typical agricultural operations manage multiple on-farm resources including soil, nutrients
and water. This study suggests that the on-farm solar resource management could also be
implemented for productive benefits. Water limited areas are most likely to benefit as solar
management reduces PET and consequently the water demand. Not all crops will be amenable
to solar management, and the economics of active solar management with PV panels needs
further study. But, semi-arid pastures with wet winters may be ideal candidates for agrivoltaic
systems as supported by the dramatic gains in productivity (90%) observed over the May-Aug
2015 observation period at the Rabbit Hills agrivoltaic solar array. These net benefits were
largely achieved through an increased water use efficiency in the shaded areas of the field
which left water stored in the soil column available throughout the entire observation period.
Extreme heterogeneity and spatial gradients in biomass production and soil moisture were
observed as a result of the heterogeneous shade pattern of the PV array. Future agrivoltaic
designs should eliminate this heterogeneity by optimizing PV panel placement to create a spa-
tially uniform shadow pattern. A spatially uniform shadow pattern would foster uniform bio-
mass accumulation benefits. The agricultural benefits of energy and pasture co-location could
reduce land competition and conflict between renewable energy and agricultural production.
Reduced or eliminated land completion would open new areas for PV installation. Local cli-
matic effects of agrivoltaic installations were statistically significant but subtle, however the
regional climatic impacts (e.g. rainfall patterns) of large scale agrivoltaic instillations are still
unclear and should be the subject of further study.
Supporting information
S1 Appendix. Figure A: Wind rose plots for four level heights.
(DOCX)
S2 Appendix. Figure A: Soil moisture time series (a) 0.1m, (b) 0.3m and (c) 0.5m. For more
information: there was 40 mm precipitation over the observation period, i.e. May-Aug 2015.
(DOCX)
S3 Appendix. Figure A: Selected normalized soil moisture profiles from data sampling to
show the change in soil moisture through growing season: May 06–2015 to August 27–2015.
The dates are mentioned on top of each figure with mmddyy format.
(DOCX)
Acknowledgments
Sincere acknowledgment is addressed to Dr. Ziru Liu (Postdoc in NewAg lab) for field
assistance.
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Author Contributions
Conceptualization: John S. Selker, Chad W. Higgins.
Data curation: Elnaz Hassanpour Adeh.
Formal analysis: Elnaz Hassanpour Adeh.
Funding acquisition: Chad W. Higgins.
Investigation: Elnaz Hassanpour Adeh.
Methodology: Chad W. Higgins.
Project administration: Chad W. Higgins.
Supervision: Chad W. Higgins.
Writing – original draft: Elnaz Hassanpour Adeh.
Writing – review & editing: John S. Selker, Chad W. Higgins.
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