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Nature Sustainability
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https://doi.org/10.1038/s41893-025-01546-4
Analysis
Impacts of agrisolar co-location on
the food–energy–water nexus and
economic security
Jacob T. Stid 1 , Siddharth Shukla 2, Anthony D. Kendall 1,
Annick Anctil 2, David W. Hyndman 3, Jeremy Rapp1 & Robert P. Anex 4
Understanding how solar PV installations aect the landscape and its critical
resources is crucial to achieve sustainable net-zero energy production. To
enhance this understanding, we investigate the consequences of converting
agricultural elds to solar photovoltaic installations, which we refer to as
‘agrisolar’ co-location. We present a food, energy, water and economic
impact analysis of agricultural output oset by agrisolar co-location for
925 arrays (2.53 GWp covering 3,930 ha) spanning the California Central
Valley. We nd that agrisolar co-location displaces food production but
increases economic security and water sustainability for farmers. Given the
unprecedented pace of solar PV expansion globally, these results highlight
the need for a deeper understanding of the multifaceted outcomes of
agricultural and solar PV co-location decisions.
Climate change threatens our finite food, energy and water (FEW)
resources. To address these threats by transitioning towards net-zero
carbon emissions energy systems, new energy installations should
be designed while considering effects on the complete FEW nexus.
The rapid expansion of solar photovoltaic (PV) electricity generation
is a key part of the solution that will need to grow more than tenfold
in the United States (US) by 2050 to meet net-zero goals
1
. However,
solar PV expansion presents threats to agricultural production due
to its land-use intensity and potential in croplands
2
. A considerable
portion of ground-mounted solar PV facilities in the US are installed in
agricultural settings3–5. Yet regions with high solar breakthrough, such
as the California Central Valley (CCV), are often among the most valu-
able and productive agricultural land in the US
3,5,6
. It is not yet clear how
the current solar PV landscape affects agricultural security, much less
under 2050 net-zero expansion. Here we quantify both the agricultural
offsets of solar PV land-use change and the decision-making processes
behind these transitions for existing solar PV arrays in agriculture.
Competition between solar PV and agricultural land uses has led
to various co-location methods where installations are sited, designed
and managed to optimize landscape productivity across a wide range
of ecological and anthropogenic services7. This approach differs
from conventional solar PV deployment, which is often installed and
managed primarily for electricity output and reduced maintenance7.
Emerging concepts such as techno-ecological synergies (TES)
8
and
more recently, ecovoltaics7, encompass a wide range of co-location
strategies enabling renewable energy installations to serve multiple
productive ecosystem services. Agricultural production and solar PV
can be laterally integrated (agrisolar co-location)
9
or directly share
land and photons via vertical integration (agrivoltaic co-location)10,11.
Agrivoltaic co-location involves the direct integration of solar and
agriculture (crops or grazing) or ecosystem services (pollinator habi-
tat, native vegetation) within the boundaries of solar infrastructure
11
.
The earliest technical standardization, originating from Germany,
specifies that this can occur under or between system rows, but not
adjacent to, while agricultural yield losses are reduced to less than
one-third of reference (without solar PV) yields10. Effective agrivoltaic
management can improve agricultural yield, microclimate regulation,
soil moisture retention, nutrient cycling and farmer profitability,
Received: 21 October 2023
Accepted: 13 March 2025
Published online: xx xx xxxx
Check for updates
1Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, USA. 2Department of Civil and Environmental Engineering,
Michigan State University, East Lansing, MI, USA. 3Department of Sustainable Earth System Sciences, School of Natural Sciences and Mathematics,
The University of Texas at Dallas, Richardson, TX, USA. 4Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
e-mail: stidjaco@msu.edu
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Analysis https://doi.org/10.1038/s41893-025-01546-4
land. The purpose of this analysis is to evaluate the lifespan FEW and
economic impacts of existing agrisolar arrays in the CCV. Rather than
projecting future installations or policies, we report on the existing
agrisolar placement, design and policy practices to inform future
practices on a per-hectare basis, tailored to regional needs. We also
highlight the need for, and opportunities within, additional research
into agrisolar practices.
Results
Commercial- and utility-scale agrisolar arrays in CCV
We assembled a comprehensive dataset of agriculturally co-located
solar PV installations within the CCV through 2018. We identified 925
solar PV arrays installed between 2008 and 2018, with an estimated
capacity of 2,524 MW
p
on 3,930 ha of recently converted agricultural
land. The estimated array capacity of each individual array ranged
from 19 kWp to 97 MWp. A temporal synthesis of the input solar PV
dataset, separated by array scale, is shown in Fig. 2b,c. The smaller
commercial-scale arrays are roughly twice as common, yet account
for one-tenth of the installed capacity and converted land area of
utility-scale arrays. Note that commercial-scale arrays are predomi-
nantly fixed axis, whereas utility-scale arrays are more frequently
single-axis tracking systems. There are also notable peaks in the number
of installations for both array scales in 2016, potentially in response to
the NEM 2.0 legislation timeline21. While there is some spatial cluster-
ing of converted crop types (Fig. 2a), converted crops were widely
distributed across the CCV.
Offset food and nutritional production
The 925 agriculturally co-located arrays displaced 3,930 ha of cropland,
which is ~0.10% of the CCV active agricultural land
22
. In the baseline sce-
nario (Methods provide scenario details), nutritional loss was 0.16 tril
-
lion kcal (Tkcal) and 1.41 Tkcal foregone by commercial- and utility-scale
arrays, respectively (Fig. 3). The total, 1.57 Tkcal, is equivalent to the
caloric intake of ~86,000 people for 25 years (solar lifespan), assuming a
2,000 kcal d–1 diet. The nutritional footprint of commercial-scale arrays
(−21.2 million kcal (Mkcal) ha
–1
yr
–1
) was greater than utility-scale arrays
(−15.6 Mkcal ha
–1
yr
–1
) and the total impact was primarily composed
of grain (58%), orchard crops (21%) and vegetables (10%). Utility-scale
arrays displaced the nutritional value of grain (60%) hay/pasture (16%)
and vegetables (10%). Note that for displaced kcal production of hay/
pasture, contribution was negligible despite dominating the converted
area due to inefficient caloric conversion to human nutrition for feed
while enhancing public acceptance
12–15
. Thus, agrivoltaic co-location
can address the agricultural competition concerns created by solar
PV expansion.
The term agrisolar is more broadly defined (modified from
SolarPower Europe
9
), as the integration and co-management of solar
photovoltaics, agriculture and ecosystem services within agroen-
ergy landscapes, explicitly considering the trade-offs and co-benefits
of agricultural, environmental and socio-economic objectives. Thus
defined, agrisolar practices align with TES and ecovoltaic principles
and encompass both coincident (‘agrivoltaic co-location’) and adjacent
co-location where agricultural land is replaced (hereafter ‘agrisolar
co-location’)
11,16
. However, replacing agricultural land with solar PV
(‘adjacent agrisolar’) without implementing agrivoltaic management
has historically been considered conventional solar and thus excluded
from co-location research because agricultural production is ceased
on site10. There is some evidence, however, that converting portions of
agricultural fields to solar PV in water-stressed regions can also provide
water and economic benefits that enhance agricultural security despite
food production losses
17,18
. Adjacent agrisolar replacement appears to
be the dominant practice, with recent work showing that there have
been relatively few documented agrivoltaic installations compared to
total solar PV deployment in agriculture in the CCV
5,19
. Because agriso-
lar practices are understudied relative to literature on other forms of
co-location
14,20
, there is a need to assess regional resource outcomes
for most existing solar PV installations and consequences for lost food
production without agrivoltaic management. Conceptual examples of
solar PV co-location are shown in Fig. 1.
We argue that by enhancing water, energy and economic secu-
rity, transitioning farm fields to solar PV installations can be con-
sidered adjacent agrisolar management in water-stressed regions.
Here security is the capacity of a farmer to maintain or improve their
financial well-being, operational resilience and access to essential
resources, such as water and energy, while preserving the integrity
and future of their agricultural practices. We assess the FEW security
effects of these agrisolar PV installations across the CCV through 2018
and estimate the economic potential of those arrays throughout a
25-year operational-phase lifespan. We compute landowner cash
flow including net energy metering (NEM) for commercial-scale
PV installations and land leases for larger utility-scale arrays. All
resource and economic effects are referenced to a counterfactual
business-as-usual scenario with no solar PV installation, assuming
continued agricultural production and operation on the same plot of
Adjacent agrisolar co-location Agrivoltaics and ecovoltaics
Fig. 1 | Conceptual diagram of trade-offs and co-benefits with agrisolar,
agrivoltaic and ecovoltaic co-location. Farms practicing adjacent agrisolar
co-location exchange food production for enhanced energy, water and
economic resource security (left). Agrivoltaic and ecovoltaic co-location
provide additional benefits (non-exhaustive) to food, ecology, soil health and
community acceptance (right). Credit: B. McGill under a Creative Commons
license CC BY-NC-ND 4.0.
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Analysis https://doi.org/10.1038/s41893-025-01546-4
and silage crops. Resource footprint, total lifespan impact and crop
contribution is shown in Fig. 3. Cumulative resource impacts across
the region through time are available in Supplementary Fig. 1.
Electricity production and consumption
We modelled the annual electricity generation for each array and offset
irrigation electricity demand. Total cumulative electricity genera-
tion for these identified arrays by 2042 was projected to be 10 TWh
for commercial-scale arrays and 113 TWh for utility-scale arrays. The
potential electricity saved by not irrigating converted land was 11 GWh
and 146 GWh for commercial- and utility-scale arrays, respectively. Note
that this was three orders of magnitude less than the total electricity
generation. For reference, the total lifespan impact of electricity pro-
duction and potential irrigation electricity offset ( ~ 124 TWh) could
power ~466,000 US households for 25 years (assuming 10.6 MWh yr–1
per household).
Changes in water use
Most (74%) agriculturally co-located arrays in the CCV replaced irri-
gated croplands. On the basis of the business-as-usual change in
total water-use budget (considering irrigation water-use offset and
operation and maintenance—O&M water use), we estimate that agri-
solar co-location in the region would reduce water use by 5.46 thou-
sand m3 ha–1 yr–1 (total: 42.1 million m3) and 6.02 thousand m3 ha–1 yr–1
(total: 544 million m
3
) over the 25-year period for commercial- and
utility-scale arrays, respectively. This could supply ~27 million peo-
ple with drinking water (assuming 2.4 liters per person per day) or
irrigate 3,000 hectares of orchards for 25 years. O&M water use on
previously irrigated land was ~eight times less than irrigated crops—
if offset irrigation water were conserved rather than redistributed.
Irrigated crops that contributed the most to the offset irrigation
water use were orchards (29%), hay/pasture (28%) and grain (27%) for
commercial-scale installations and grain (37%), hay/pasture 31%), cot-
ton (15%) for utility-scale installations.
Agricultural landowner cash flow
Adjacent agrisolar co-location is more profitable than the baseline
agriculture-only scenario, regardless of how landowners are compen-
sated (Fig. 4). For commercial-scale arrays, agrisolar landowners experi-
ence early losses from installation expenditure (−US$53,000 ha
–1
yr
–1
).
However, the lifespan cash flow was dominated by NEM, offset electric-
ity costs and surplus generation sold back to the grid, resulting in a net
positive economic footprint of US$124,000 ha–1 yr–1, 25 times greater
returns than lost food revenue (−US$4,920 ha–1 yr–1). The resulting
economic payback period was 5.2 years (best- and worst-case payback
in 2.9 and 8.9 years respectively; Supplementary Fig. 2).
The net economic footprint for utility-scale agrisolar landown-
ers (US$2,690 ha
–1
yr
–1
) was 46 times less than the commercial-scale
footprint (Fig. 4b). In contrast to commercial-scale arrays,
utility-scale agrisolar landowners were not responsible for instal-
lation or O&M costs but still lost food revenue (−US$3,330 ha–1 yr–1)
and were only compensated by land lease (US$1,940 ha
–1
yr
–1
) and
offset operational (US$3,830 ha
–1
yr
–1
) and irrigation water-use costs
(US$220 ha–1 yr–1). In the worst-case scenario, the total budget was
negative (−US$432 ha–1 yr–1), suggesting that some landowners could
lose revenue. There was no payback period for utility-scale agrisolar
landowners because the net economic budget was always positive
(baseline and best-case scenario) or always negative (worst-case
scenario). Cumulative economic impacts across the region in Sup-
plementary Fig. 3.
0
20
40
60
0
5
10
15
20
0
25
50
75
100
0
0.2
0.4
0.6
Installation year
2008 2010 2012 2014 2016 2018
Commercial scale: 572 arrays and 228 MW
Number of installations
Utility scale: 353 arrays and 2,296 MW
Fixed axisSingle axis
b
c
Average array capacity MW
Grain
Hay/pasture
Cotton
Orchards
Grapes
Vegetables
Other crops
a
N
km
0 60 120
Fig. 2 | Study area and characterization of ground-mounted agrisolar PV
installations. a, Map of displaced crop groups within the CCV alluvial boundary.
b,c, The array installation number, capacity, area and mount type (fixed-axis
or single-axis tracking) by year for the 925 utility- (b) and commercial-scale (c)
arrays assessed. Maps in a generated with Uber H3108 with CCV alluvial boundary
data from the US Geological Survey59 and contiguous US shapefiles from the
US Census Bureau109.
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Analysis https://doi.org/10.1038/s41893-025-01546-4
On average, estimated foregone farm operation costs exceeded
forgone food revenue (Fig. 4). While this may be affected by reporting
differences in agricultural revenue and farm operation cost sources,
agricultural margins are known to be small, or negative, for certain
croplands (for example, pastureland), with margins likely to decrease
further under future climate change and water availability scenarios
23
.
For commercial-scale installations, cutting farm operation costs in half
(highly conservative) resulted in a longer economic payback period of
just a month. Cutting offset farm operation costs in half for utility-scale
installations did not affect economic payback or the always-positive
baseline and best-case budget.
Discussion
The effect of agrisolar co-location on food production
We found that displacing agricultural land with solar PV locally
reduced crop production ( ~ 1.57 Tkcal), which may affect county-
and state-level food flows. Fortunately, on national and global scales,
food production occurs within a market where reduced production in
one location creates price signals that can stimulate production else-
where. For example, high demand and increased irrigation pumping
costs in the CCV have resulted in higher prices received for specialty
orchard crops. Thus, farmers have elected to switch from cereal and
grain crops to specialty crops24. Solar PV is also far more energy dense
per unit of land than growing crops to produce biofuels18—a practice
common across large swaths of agricultural farmland in the US and
elsewhere. We show that conversion of feed, silage and biofuel crop-
lands provides high irrigation water-use offsets while minimizing
nutritional impacts due to the low or non-existent caloric conversion
efficiencies of these crops (Fig. 3). Though, considering food waste
and a lack of crop-specific nutritional-quality knowledge, we cannot
evaluate end-point impacts of reported foregone kcal (calories) on
human diets and health25.
California produces 99% of many of the nation’s specialty fruit and
nut orchard crops (for example, almonds, walnuts, peaches, olives)
26
.
Fields producing these crops were commonly converted to solar PV
(270 ha of orchard crops), and it may be difficult to shift production of
these crops to other locations due to their intensive water footprint,
climate sensitivity and time to production27,28. Altering global supply
of these crops could lead to food price increases similar to biofuel
land-use changes29 with agricultural markets taking time to compen-
sate30. We found that these nutritionally dense, valuable and operation-
ally costly crops are more commonly replaced by commercial-scale
rather than utility-scale installations, resulting in a higher nutritional
footprint at the site scale (Fig. 3). However, due to their smaller arrays
size (Fig. 2), these arrays have a lower regional lifespan nutritional
impact. The total solar PV area we consider (the area covered by panels
and space between them) does not account for total cropland trans-
formation by all solar energy infrastructure. Thus, total cropland area
converted and associated caloric losses may be underestimated by up
to 25%. We conducted a sensitivity analysis on this potential area bias
for all area-based metrics and discuss the details of this underestimate
in Supplementary Discussion.
Global food needs are projected to double by 205031,32. To meet
these needs, yield per unit area must increase, agricultural land area
under production must increase and/or food waste and inefficiency
must be reduced. Reducing waste is feasible but requires a consider-
able change in dietary preferences
33
and supply chain pathways
34
.
Yield increases alone are unlikely to meet these needs
31
and half of
global habitable land is already agricultural
35
. Cultivated lands are
facing additional pressures due to soil quality deterioration, aridifi-
cation, water availability, urban growth and threats to global biodi-
versity that will be exacerbated under a changing climate36–39. Given
these pressures on arable land, cropland selection for future agrisolar
co-location, both commercial- and utility-scale, should be assessed
Resource Footprint
units ha–1 yr–1
Lifespan impact Crop contribution
base (range) proportion of lifespan impact
UtilityCommUtilityComm
UtilityCommUtilityCommUtilityComm
Economic
Water
Energy
Food
Land
(–1.81, –1.09)
(–0.21, –0.13)
(4.40 | 4.33)
(0.44 | 0.53)
(102, 142)
(8.93, 12.3)
(438, 650)
(32.4, 51.8)
(–39.1, 531)
(641, 1,380)
–1.41 T kcal
–0.16 T kcal
3.62 k ha
0.31 k ha
114 T Wh
10.0 T Wh
544 M m3
42.1 M m3
243 M US$
959 M US$
–15.6 M kcal
–21.2 M kcal
—
—
1.26 G Wh
1.30 G Wh
6.02 k m3
5.46 k m3
2.69 k US$
124 k US$
Grain Hay/pasture Orchards Grapes Vegetables Other cropsCotton
Fig. 3 | Lifespan land use, food loss, electricity production and potential
irrigation electricity offset and potential water conservation with agrisolar
co-location in California’s Central Valley. Scientific metric prefixes are
thousands (k), millions (M), billions (G) and trillions (T). Footprints are area-
weighted average values for the baseline scenario across commercial- (Comm)
and utility-scale (Utility) agrisolar installations. Total impacts show the baseline
scenario with worst- and best-case scenarios in parentheses, except for land area,
which shows the Ong et al.110 and fire code buffer area bias estimates, respectively
(Supplementary Discussion). Energy and water resources are the sum of total
impacts (‘Energy’ is electricity produced and irrigation electricity offset, ‘Water’
is irrigation water use offset and O&M water use). Crop contribution is ordered
by decreasing impact. Vegetables were omitted from the utility-scale economic
crop contribution because their total impact was negative (6.98% of the absolute
utility-scale economic budget), that is, replacing vegetable fields with utility-
scale arrays reduced farm income. Artwork credit: B. McGill under a Creative
Commons license CC BY-NC-ND 4.0.
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Analysis https://doi.org/10.1038/s41893-025-01546-4
at local, regional, national and international scales to maintain food
availability and security.
Water security potential with agrisolar co-location
Here we show that solar PV installations preferentially displace irrigated
land in the CCV (3,310 ha and 74% of co-located installations). Displac-
ing this irrigated cropland enhances farmer cash flow while probably
reducing overall water use by 5.46 and 6.02 thousand m
3
ha
–1
yr
–1
for
commercial- and utility-scale arrays, respectively. The total displaced
irrigation water use was eight times the O&M use for those arrays. Thus,
installing solar PV in water-scarce regions has substantial potential to
reduce water use, which bolsters findings from previous studies
17,18,40,41
.
This analysis does not incorporate the additional hydrologic effects
of modifying surface energy and water budgets, including reducing
evapotranspiration and the potential for increased groundwater
recharge42,43.
Given that the cash flow benefits from utility-scale agrisolar
co-location are relatively small, we evaluated how water-use limita-
tions may be a factor in farmland conversion decisions. We hypothesize
that fallowing land is largely a consequence of water shortages in the
CCV24,40, thus fallowing land proximal to an array (within 100 metres)
may indicate an emergent agrisolar practice: intentional fallowing
and irrigation water-use offset adjacent to arrays supported by rev-
enue from the array. Each array was coded by the adjacent crop type
before and post installation of the array. While we cannot know what
landowners would have done with the array acreage absent the instal-
lation, this analysis provides evidence of broader land-use trends that
might have been driving decisions. The transition of array acreage
from before proximal post-installation land use for utility-scale arrays
is displayed in Fig. 5.
Understanding how economic incentives affect the replacement
of valuable cropland with solar PV is essential to inform future energy
landscape models and policies. Here we examined the transition
to post-solar installation fallowing in adjacent irrigated cropland
(Fig. 5). We observed fallowing of adjacent irrigated cropland at
58 utility-scale installations totalling 658 MW
p
and 968 ha (27% of
utility-scale area) composed of 410 ha of grain, 250 ha of hay and
pasture, 225 of orchards, grapes and vegetables and 82 ha of cotton
and other crops. The direct area of these arrays (968 ha) can be linked
to a potential irrigation water-use offset of 195 million m3 over 25
years. If these arrays were on-farm plots of average size, 14,000 ha of
fallowed land adjacent to these 58 arrays could displace an additional
120 million m3 of irrigation water use, each year, or 3,000 million m3
over 25 years (Supplementary Methods). Thus, if landowners choose
to fallow farmland adjacent to leased land for utility-scale arrays,
the water-use reductions are greatly amplified. We discuss several
important limitations44 of the Cropland Data Layer (CDL) regarding
this analysis in Supplementary Discussion.
Intensely irrigated cropland in the CCV is vulnerable to drought,
especially in southern basins that rely heavily on surface-water deliver-
ies due to limited groundwater availability
45
. The California Budget Act
of 2021 provides financial support for fallowing to motivate farmers
to reduce water use46. Whereas fallowing land can help mitigate some
hydrological problems, removing production can also result in large
agricultural revenue losses
47
. Converting land with solar electricity
production, rather than simply fallowing could reduce risks to farm-
ers while enhancing financial security
17
, especially during periods of
extreme drought40. Whereas this has implications for future installa-
tions, we show that farmers already appear to be practicing solar fal-
lowing, probably resulting in long-term irrigation water-use reductions.
We acknowledge the potential issues in assuming that fore-
gone irrigation water use due to solar PV installations was conserved
rather than redistributed. However, a portion of this potential offset
is probably real given three observations: (1) utility-scale installa-
tions correlate with newly fallowed land, which was not observed for
commercial-scale arrays; (2) the 2014 Sustainable Groundwater Man-
agement Act (SGMA)48 requires water-use reductions by the 2040s and
(3) agriculturally co-located solar PV maintains Williamston Act Status
under the Solar-Use Easement
49
(which has recently been revived
50
),
a California tax incentive common in irrigated lands highly suitable
for solar
51
. In our dataset, 46% of utility-scale installations and 58%
of commercial-scale installations were completed after SGMA was
enacted (Fig. 2b,c). We also performed a sensitivity analysis where
only 50% of irrigation water-use offset was conserved rather than
redistributed, which still resulted in an estimated US$9 million and
246 million m
3
conserved due to the regional change in water use from
just direct area converted (Supplementary Discussion).
Given this potential for water-use offset, solar fallowing for
water-use reduction presents an opportunity for incentivized solutions
that are already of interest to landowning farmers in the region
17
. With
suitable solar area in the CCV exceeding projected fallowing acreage
to comply with SGMA
51
, implementing agrisolar co-location policies
and incentives such as these could promote complementary land uses
and enhance public support15.
181.00
5.84 0.20
–52.50
–4.96 –4.92
NEMOperation Water Installation O&M Food Profit LeaseOperation Water Food Profit
124.00
0
50 Worst case
Baseline
Best case
100
150
200
1.94
3.83
0.22
–3.30
2.69
0
2
4
6
Economic footprint (k US$ ha–1 yr−1)
Discounted cash flow source
Utility scaleCommercial scale
a bRevenue Expenditure Net NetRevenue Exp.
Fig. 4 | Lifespan economic footprint of commercial- and utility-scale agrisolar
co-location. a,b, The discounted cash flow footprint for commercial- (n = 572;
a) and utility-scale (n = 353; b) agrisolar in thousand US$ ha–1 yr–1. Data are
represented as the baseline area-weighted mean footprint, with vertical lines
used to illustrate the range between best- and worst-case scenarios. Discounted
cash flows are broken into revenues (blue), expenditures (Exp.) (orange) and net
profits (green). Variable explanation in equations (9) and (10) in ‘Discounted cash
flow for agrisolar co-location’.
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Achieving economic security across return structures
Regardless of scale and related financial benefits, farmers are switching
away from cultivating crops to cultivating electricity. This study empiri-
cally demonstrates that both NEM and land-lease incentive structures
have been viable frameworks for PV deployment in some of the most
valuable cropland in the US6. Critically, we incorporate farm-specific
agricultural dynamics across a region (offset farm operation costs,
irrigation costs and food revenue) into economic considerations for
replacing cropland with solar.
By including these revenues and costs, this study clearly dem-
onstrates the strong economic incentives to replace cropland with
commercial-scale arrays (Fig. 4a). Under the grandfathered NEM 1.0
and 2.0 agreements, commercial-scale agrisolar landowners enhanced
financial security by 25 times lost food revenue over the lifetime of the
array, while simultaneously reducing water use. The resulting total net
revenue, US$124,000 ha
–1
yr
–1
, is potentially underestimated because
post-lifespan module replacement, resale or continued use is likely,
and property values could increase (terminal value) compared to the
reference scenario. We also have not considered several programmes,
credits and incentives (for example, Rural Energy for America Pro-
gram) that could enhance net revenue (Supplementary Discussion).
However, these returns are not unlimited due to NEM capacity limita-
tions (<1 MWp) and requirements to size the installation below annual
on-farm load21.
Renewable energy policy evolves quickly, shifting incentives for
new customer generators. Whereas climate change and decreasing
water availability in the coming decades
23
will probably increase finan-
cial motivation to install solar in agriculture, future adoption and the
co-benefits reported here will also depend on new business models
for grid pricing52. Pricing structures have already and will inevitably
continue to change as utilities, regulators and grid customers adapt
to distributed renewable generation, avoid curtailment and avoid
the utility death spiral52. Although future installations and policy are
not the focus of this study, the newest policy, NEM 3.0, substantially
reduces compensation for surplus generation and limits options for
multiple metered connections53, probably requiring future installa-
tions to add battery storage and other measures to maintain similar
profitability
54
. However, this study considers solar arrays that are
grandfathered into their respective NEM 1.0 and 2.0 agreements.
Additionally, our estimated load contributions suggest that revenue
reported here mostly originates from offset demand rather than
credit for surplus generation (Supplementary Notes and Supplemen-
tary Discussion). The bottom line is that owning solar PV, offsetting
annual on-farm electric load and selling surplus electricity back to
the utility under NEM 1.0 and 2.0 has increased economic and energy
security for farmers with existing arrays and has probably promoted
water-use reductions in the region. Importantly, we also assumed
that all decisions were made by and returns received by landowning
or partial-owning farmers. We do not have access to land-ownership
data for the CCV, but nearly 40% of agricultural land in the region is
rented or leased55.
Utility-scale land-lease rates alone do not offset lost agricultural
revenue. However, including offset farm operation costs results in a
substantially lower but still profitable agrisolar economic footprint
with no major up-front capital investment (Fig. 4b). In water-scarce
regions, particularly where water-use reduction is required, the smaller
returns from utility-scale agrisolar practices and potentially related
fallowing of land may be more attractive than continued cultivation
under water-supply uncertainty
17
. Thus, without profitable compen-
sation, agrivoltaic practices may not be feasible if offset operational
costs and water-use reductions are driving utility-scale agrisolar deci-
sion making. We also omit some agricultural dynamics (such as the
environmental benefits of carbon reduction), which could reinforce
resource and economic security for both commercial- and utility-scale
installation (Supplementary Discussion).
Opportunities for agrisolar research
Whereas funding and incentives for co-location research have
expanded rapidly in recent years, we advocate extending these to
agrisolar co-location. Adjacent agrisolar replacement with barren or
unused ground cover still falls short of the full potential of ecovoltaic
and agrivoltaic multifunctionality7,9–11. However, the regional resource
and economic co-benefits of replacing irrigated land in water-stressed
regions with solar PV here cannot be ignored. These findings are also
immediately relevant to the Protecting Future Farmland Act of 2023
56
,
which set out a goal to better understand the multifaceted impacts of
installed solar on US agricultural land. We discuss additional placement
and management decisions that fall under the umbrella of agrisolar
co-location in Supplementary Discussion.
We have shown that the goal of co-location, to enhance synergies
between the co-production of agriculture and/or other ecosystem
services and net-zero electricity production, is at least partially achiev-
able with agrisolar co-location. Broader agrisolar research may also
expose the consequences of not widely adopting agrivoltaics to retain
agricultural production and protect food security. Given the ecosystem
service benefits reported here, there may be an opportunity to broaden
Cotton and
other crops
Hay/pasture
Grain
Orchards,
grapes and
vegetables
Fallow/idle
Cotton and other
Hay/pasture
Grain
Orchards,
grapes and
vegetables
0
300
600
900
1,200
1,500
1,800
2,100
2,400
2,700
Pre-installation Post-installation
Array area (ha) and adjacent land cover
Fallow/idle
Fig. 5 | Land-use change adjacent to utility-scale solar PV installations on
previously irrigated cropland in the CCV. Note several crop types are grouped
for simplicity and thus have altered colouring compared to similar groups in
other figures. Transitions with total array capacity of <10 MWp were omitted for
clarity but are shown in Supplementary Table 1.
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the scope of co-location research and incentives to include agrisolar
co-location practices defined here.
Methods
Identifying agrisolar PV arrays across the CCV
We used remotely sensed imagery of existing solar PV arrays and geo-
graphic information system (GIS) datasets to develop a comprehensive
and publicly available dataset of ground-mounted arrays co-located
with agriculture in the CCV through 2018. We extracted all existing
non-residential arrays from two geodatabases (Kruitwagen et al.
4,57
and Stid et al.5,58) within the bounds of the CCV alluvial boundary59.
We removed duplicate arrays and applied temporal segmentation
methods described in Stid et al.
5
to assign an installation year for Kruit-
wagen et al.4 arrays. We acquired Kruitwagen et al.4 panel area within
array bounds by National Agriculture Imagery Program imagery pixel
area with solar PV spectral index ranges suggested in Stid et al.
5
and
removed commissions (reported array shapes with no panels). We
then removed arrays with >70% overlap with building footprints60 to
retain only ground-mounted installations. Finally, overlaying histori-
cal CDL crop maps with new array shapes, we removed arrays in areas
with majority non-agricultural land cover the year before installation
(Supplementary Fig. 4 and Supplementary Discussion).
The resulting dataset (925 agrisolar co-located arrays) included
686 ground-mounted arrays from Stid et al.
5
plus 239 from Kruitwagen
et al.4. For these sites, we calculated array peak capacity (kWp) by61:
Capacity =Areapanel ×η×GSTC (1)
where
Areapanel
is the total direct area of PV panels in m
2
,
η
is the average
efficiency of installed PV modules during the array installation year62
(Supplementary Fig. 5) and
GSTC
is the irradiance at standard test condi-
tions (kW m–2). Arrays were split into ‘Commercial-’ (<1 MWp) and
‘Utility-’ (≥1 MW
p
) scale arrays following the California Public Utility
Commission NEM capacity guidelines63.
Scenario summary and assumptions
We computed annual FEW resource and economic values for each
ground-mounted agrisolar PV array identified across the CCV for four
scenarios: (1) reference, business as usual with no solar PV installation
and continued agricultural production on the same plot of land, (2)
baseline, agrisolar PV installation with moderate assumptions related
to each component of the analysis, (3) worst case, PV installation with
high negative and low positive effects for each component, (4) best
case, similar but opposite of the worst-case scenario. We compare
baseline to the reference scenario to estimate the most likely FEW and
economic effects and use the differences between best- and worst-case
scenarios to estimate uncertainty. Supplementary Tables 2 and 3 pro-
vide an overview of scenarios for each resource and Supplementary
Tables 4 and 5 for baseline agrisolar lifespan FEW resource and eco-
nomic value outcomes, respectively.
Identified arrays were installed between 2008 and 2018 and
were assumed to have a 25-year lifespan for arrays due to perfor-
mance, warranties, module degradation and standards for electrical
equipment
64,65
. We assumed that land-use change effects ceased fol-
lowing 25 years of operation to simplify assumptions about module
replacement, resale or continued use. We then summarized the FEW
and economic effects of all arrays across the CCV and divided our
temporal analysis into three phases: (1) addition (2008–2018) where
arrays were arrays were being installed across the CCV, (2) constant
(2019–2032) with no array additions but all arrays installed by 2018 are
operating and maintained and (3) removal (2032–2042), where arrays
are removed after 25 years of operation.
We performed several sensitivity analyses to address limitations
in the available data and methods and to show how changes in future
policy (NEM) could affect incentives displayed here. Sensitivity analysis
included the capacity cut-off between commercial- and utility-scale
(5 MW), solar PV lifespan (15 and 50 years), nominal discount rate (3%,
7% and 10%), solar PV direct area bias (proportional direct to total
infrastructure area and a uniform perimeter buffer) and irrigation redis-
tribution (assuming 50% of irrigation water-use offset is redistributed
rather than conserved), all else equal (Supplementary Discussion and
Supplementary Tables 6–20). We discuss additional assumptions and
limitations in Supplementary Discussion.
Displaced crop and food production
Replacing fields (or portions thereof) with solar PV arrays affects
crop production by (1) lost production of food, fibre and fuels and (2)
reduced revenue from crop sales. We simplify the complex effects of
lost production and include solely the foregone calories through both
direct and indirect human consumption, which is justified because
CCV crop production is largely oriented towards food crops. Future
analyses could evaluate the lost fibre (primarily via cotton) or fuel (via
biofuel refining) production.
We evaluated the economic and food production effects of dis-
placed crops through a crop-specific opportunity cost assessment of
land-use change, incorporating actual reported; yields, revenue, caloric
density and regionally constrained caloric conversion efficiencies for
feed/silage and seed oil crops. All crop type information was derived
from the USDA National Agricultural Statistics Service (NASS) CDL
22
for
the array area in both prior- and post-installation years (Supplementary
Fig. 4 and Supplementary Methods provide the adjacent fallowed land
analysis). Each array was assigned a majority previous crop from the
spatially weighted means of crop types within the array area for the
five years before the installation.
We converted all eligible crop types to kcal (also called calorie) for
human consumption after Heller et al.
25
. Foregone food production
(
FoodForegone
in kcal) following PV installation was then defined for each
array as:
FoodForegone =kcaldensity ×Yield ×Area (2)
where
kcaldensity
is in kcal kg
–1
,
Yield
is in kg m
–2
and
Area
of each array in
m
2
. Crop-specific caloric density data (kcal kg
–1
) were derived from the
USDA FoodData Central April 2022 release66. FoodData food descrip-
tions and nutrient data were joined and CDL specific crop groupings
were made through a workflow described in Supplementary Fig. 6.
Crop-specific yield data (kg m–2) were derived from the USDA NASS
Agricultural Yield Surveys
67
. State-level (California) yield data were
processed similarly, with missing crop data filled based on national
average yields. We used caloric conversion efficiencies for feed,
silage or oil crop to account for crop production that humans do not
directly consume.
For each array, we calculated annual revenue of forgone crop
production in real (inflation adjusted) dollars, calculated by:
CropForegone =Pricecrop ×Yield ×Area (3)
where
Pricecrop
is in US$ kg
–1
,
Yield
is in kg m
–2
and
Area
of each array
in m
2
. We used the annual ‘price received’ for all crops in the USDA NASS
Monthly Agricultural Prices Report for 2008 through 201868. For the
baseline case, we assumed that food prices will scale directly with
electricity prices through 2042 given that they respond to similar
inflationary forces
69
. Supplementary Fig. 6 and Supplementary Meth-
ods provide a more complete workflow including best- and worst-case
scenario assumptions.
Change in irrigation water use and cost savings
Irrigation water use can only be offset by agrisolar co-location if the
prior land use was irrigated. The presence of irrigation was inferred
from the Landsat-based Irrigation Dataset (LanID) map for the year
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before installation
70,71
(Supplementary Fig. 4). If the array area con-
tained irrigated pixels, then we assumed the cropland area and all
respective crops within the rotation were irrigated.
We calculated the total forgone irrigation water use (
IrrigWaterForegone
in m3) by:
IrrigWaterForegone =
IrrigWater
year
IrrigWatersurvey year
×IrrigDepthcrop ×
Area (4)
where
IrrigDepthcrop
in m is the crop-specific irrigation depth,
IrrigWateryear
in m3 is the annual county-level irrigation water-use esti-
mate and
IrrigWatersurvey year
in m3 is the county-level irrigation water-use
estimate for the respective survey year irrigation depths.
We estimated annual crop-specific county-level irrigated depths
from survey and climate datasets for each array. Crop-specific irriga-
tion depths (
IrrigDepthcrop
) were taken from the 2013 USDA Farm and
Ranch Survey72 and 2018 Irrigation and Water Management Survey73,
and logical crop groupings were applied (for example, almonds, pis-
tachios, pecans, oranges and peaches were considered orchard crops).
Because irrigation depths depend on the total precipitation in each
survey year, we used multilinear regression to build annual county-level
irrigation water-use estimates (
IrrigWateryear
) from five-year US Geologi-
cal Survey (USGS) water use74, gridMET growing season average
precipitation
75
, with year as a dummy variable to incorporate temporal
changes in irrigation technologies and practices. For the installation
phase (2008 to 2018), these depths varied based on historical climate
and survey data, whereas the projection phases (constant and removal)
used a scenario-dependent moderate, wet (worst-case, least water
savings) or dry (best case, most water savings) year estimate from the
historical record (discussed in Supplementary Methods).
Assigning an economic value to water use is difficult and varies
based on the temporally changing supply and demand
76
. We calculated
the economic value of the change in water use (Water in real US$) to
the farmer by:
Water =(ΔWateruse ×IrrigEnergy ×PriceElec)+Waterright (5)
where
ΔWateruse
(m
3
) is the offset irrigation water use for the co-located
crop minus O&M projected water use,
IrrigEnergy
(MWh m–3) is the irriga-
tion electricity required to irrigate the co-located crop given local
depth to water and drawdown estimates from McCarthy et al.
77
,
PriceElec
(US$ MWh
–1
) is the utility-specific (commercial-scale) or regional aver-
age (utility-scale) annual price of electricity based on the electricity
returns and modelled electricity generation described in Supplemen-
tary Methods and
Waterright
is a CCV-wide average water right contract
rate of ~ US$0.03 m
–3
(ref. 78). Here we assume that water (and thus
energy) otherwise used for irrigation was truly foregone and not redis-
tributed elsewhere within or outside the farm. Change in O&M water
use was based on Klise et al.
79
reported values, described in Supple-
mentary Methods.
Electricity production, offset and revenue
Installing solar PV in fields has three benefits: (1) production of electric-
ity by the newly installed solar PV array, (2) reduction in energy demand
due to reduced water use and field activities and (3) revenue genera-
tion via net energy metering (NEM) or land lease. Here we assume that
on-farm electricity demand is dominated by electricity used for irriga-
tion and ignore offset energy (embodied) used for fuel.
We modelled electricity generation for each array using the pvlib
python module developed by SANDIA National Laboratory
80
. Weather
file inputs for pvlib were downloaded from the National Renewable
Energy Laboratory (NREL) National Solar Radiation Database
81
. We also
estimated annual on-farm load to differentiate offset electricity use
and surplus generation. Not only is electricity generated by the arrays,
but electricity consumption is foregone for each array due to not
irrigating the array area. The annual change in electricity consumption
due to water use (
Electricitywater use
in GWh) is calculated by:
Electricitywater use =IrrigElecdemand ×ΔWateruse (6)
where
IrrigElecdemand
is the county-level rates for irrigation electricity
demand in GWh m
–3
and
∆Wateruse
is the change in water use in m
3
from equation (5). County-level electricity requirements to irrigate
were calculated using irrigation electricity demand methods
described in McCarthy et al.77 modified with a CCV-specific depth to
water (piezometric surface) product for the spring (pre-growing
season) of 201882.
Revenue from electricity generation was calculated separately
for each array depending on array size and the installation year.
Commercial-scale arrays (<1 MW) were assumed to operate under an
NEM 1.0 if installed before 2017 and NEM 2.0 if installed later, which
allows for interconnection to offset on-farm load and compensation
for surplus electricity generation (Supplementary Methods and Sup-
plementary Table 21). Thus, for commercial-scale arrays, annual cash
flow from solar PV (NEM in US$) is calculated as:
NEM =Savedoffset load +Earnedsurplus (7)
where
Savedoffset load
is real US$ saved by offsetting annual on-farm
electric load and
Earnedsurplus
is real US$ earned by surplus PV electricity
generation sold to the utility under NEM guidelines. Both
Savedoffset load
and
Earnedsurplus
are estimated based on pvlib modelled electricity
generation and valued at the historical utility-specific energy charge
retail rates. Historical energy charges are available either through util-
ity reports83–85 or the US Utility Rate Database via OpenEI86. We made
several assumptions that resulted in omission of fixed charges includ-
ing transmission and interconnection costs from the analysis. Details
about electricity rates and omitted charges are summarized in Sup-
plementary Methods.
For utility-scale arrays (≥1 MW), annual revenue from agrisolar
co-location (Lease in US$) was assumed to be given by:
Lease =Landlease ×Area (8)
where Lease is the economic value estimated to be paid to the farmer by
the utility for leasing their land in US$ m–2 and Area of each array in m2.
We assumed commercial-scale arrays installed before 2017 were
grandfathered into NEM 1.0 guidelines for the duration of their lifespan.
However, arrays installed in 2017 and 2018 fall under NEM 2.0 guidelines
which include a US$0.03 kWh
–1
non-bypassable charge removed from
Earnedsurplus
21,87,88. Annual on-farm operational load was estimated and
distributed across the year based on reported California agricultural
contingency profiles89 and Census of Agriculture county-level average
farm sizes
90–92
(Supplementary Figs. 7 and 8 and Supplementary Meth-
ods). With distributed hourly load estimations and modelled solar PV
electricity generation, we delineated electricity and revenue contribut-
ing to annual load (
Savedoffset load
) from surplus electricity and revenue
that would have been sold back to the grid and credited via NEM
(
Earnedsurplus
).
Future electricity revenue was projected using 2018 conditions
(contribution to annual load, to surplus) and energy charge rates,
modelled electricity production described above (includes degrada-
tion, pre-inverter, inverter efficiency and soiling losses) and projected
changes in the price of electricity. The Annual Energy Outlook report by
the US Energy Information Administration (EIA) provides real electric-
ity price projections annually between 2018 and 2050 for ‘Commercial
End-Use Price’93. This annual rate of change was used to estimate pro-
jected deviations from 2018 energy charges (2018 US$ kWh–1) during
the constant and removal phases (2019–2042), with projected solar PV
generation including discussed losses.
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Nature Sustainability
Analysis https://doi.org/10.1038/s41893-025-01546-4
We used solar land consultant and industry reports for solar
land-lease (
Landlease
) rates that ranged from US$750 ha–1 yr–1 to
US$4,950 ha–1 yr–1, with high-value land averaging IS$2,450 ha–1 yr–1 in
the CCV
94,95
. Comparable lease rates (~US$2,500 to US$5,000 ha
–1
yr
–1
)
were reported by developers in the CCV region17 and used in a solar PV
and biomass trade-off study in Germany18 (~US$1,000 to
US$2,950 ha–1 yr–1).
Array installation and O&M costs
Historical installation costs (Installation) were taken from the
commercial-scale PV installation prices reported in the Annual Tracking
the Sun report where reported prices are those paid by the PV system
owner before incentives
62
. The baseline scenario is the median installa-
tion price, whereas the best- and worst-case scenarios are the 20th and
80th percentile installation costs, respectively. These reported values
are calculated using NREL’s bottom-up cost model and are national
averages using average values across all states. Installation cost was not
discounted, as it represents the initial investment for commercial-scale
installations at day zero. All future cash flows, profits and costs are
compared to this initial investment. We also included the 30% Solar
Investment Tax Credit in the Installation for commercial-scale arrays
96
.
The system bounds of this impact analysis were installation through
the operational or product-use phase. We, therefore, did not assume
removal expenses or altered property value (terminal value) to remove
uncertainty in decision making at the end of the 25-year array lifespan.
Historically reported and modelled O&M values (pre-2020) range
from US$0 kWp–1 yr–1 (best case) to US$40 kWp–1 yr–1 (worst case) with
an average (baseline) of US$18 kWp–1 yr–1 (refs. 97,98). Projected O&M
costs were based on modelled commercial-scale PV lifetime O&M cost
to capital expenditure cost ratios from historical and industry data that
provided scenarios varying on research and development differences
(conservative, moderate, advanced). The annual reported values are
provided from 2020 to 2050 for fixed O&M costs including: asset
management, insurance products, site security, cleaning, vegetation
removal and component failure and are detailed in the Annual Tech-
nology Baseline report by NREL97, which are largely derived from the
annual NREL Solar PV Cost Benchmark reports.
Farm operation costs
Business-as-usual farm operation costs (Operation) were derived from
the ‘Total Operating Costs Per Acre to Produce’ reported in UC Davis
Agricultural and Resource Economics Cost and Return Studies
99
. We
removed operational costs to ‘Irrigate’ from the total because we esti-
mate that as a function of electricity requirements and water rights
(described in ‘Change in irrigation water use and cost savings’) while
retaining ‘Irrigation Labour’ as this was not included in our irrigation
cost assessment. Best- and worst-case scenarios for farm operation
costs coincided with yield scenarios described in ‘Displaced crop and
food production’.
Discounted cash flow for agrisolar co-location
For each commercial-scale array in the CCV, we computed the annual
real cash flow as:
Commercial =NEM +Water +Operation −Food −O&M−Installation
(9)
and for each utility-scale array as:
Utility =Lease +Water +Operation −Food (10)
where Commercial is the real return in 2018 US$ for commercial-arrays
(<1 MWp) and Utility is the real return in 2018 US$ for utility-scale
arrays (≥1 MWp). Each of the terms on the right-hand side of these
equations are defined in the sections above.
We then computed real annual discounted cash flow (
DCFreal
) for
each array to estimate the total lifetime value of each array. The
DCFreal
at any given year n is calculated for each array by:
DCFreal
25
n1
CF
real
n
1rreal
n
(11)
where
CFn
real
is the real annual cash flow at year n (either Commercial
or Utility as relevant for each array) and
rreal
is the real discount rate
without an expected rate of inflation (i) from the nominal discount rate
(
rnom
) calculated using the Fisher equation100:
rreal =
(1+r
nom
)
(1+i)
−
1 (12)
Vartiainen et al.101 clearly communicates this method in solar PV
economic studies and discusses the importance of discount rate (in
their case, weighted average cost of capital) selection. For i, we use 3%,
which is roughly the average producer price index (PPI) and consumer
price index (CPI) (3.4% and 2.4%, respectively) between 2000 and 2022
and comparable to other solar PV economic studies101,102. We use a 5%
rnom
103
and perform a sensitivity analysis using 3%, 7% and 10%
rnom
and
discuss discount rates used in literature in Supplementary Discussion.
Separately from the sensitivity analysis for
rnom
, we also calculated our
best-case and worst-case scenarios for each array.
All prices were adjusted to 2018 US dollars for calculation of real
cash flow terms in equations (11) and (9). We adjusted consumer elec-
tricity prices and installation costs for inflation to real 2018 US$ using
the US Bureau of Labor Statistics Consumer Price Index for All Urban
Customers104. We adjusted all production-based profits and costs (all
other resources) using US Bureau of Labor Statistics Producer Price
Index for All Commodities105.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
The datasets and outputs generated in the current study are publicly
available via Zenodo at https://doi.org/10.5281/zenodo.10023293
(ref. 106) with all source data referenced in the published article and
in its Supplementary Information files.
Code availability
The code used to generate and analyse the datasets reported here are
hosted via GitHub at https://github.com/stidjaco/FEWLS_tool and
are available via Zenodo at https://doi.org/10.5281/zenodo.10023281
(ref. 107).
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Acknowledgements
This work was supported by the USDA National Institute of Food
and Agriculture (NIFA) INFEWS grant number 2018-67003-27406.
We credit additional support from the USDA NIFA Agriculture and
Food Research Initiative Competitive grant number 2021-68012-
35923 and the Department of Earth and Environmental Sciences at
Michigan State University. Any opinions, indings and conclusions
or recommendations expressed in this publication are those of the
authors and do not necessarily relect the views of the USDA or
Michigan State University. We are grateful to B. McGill for bringing
the vision of agrisolar co-location to life through her artistic
conceptual depiction.
Author contributions
J.T.S. led the dataset characterization, methods development, analysis,
interpretation and wrote the original draft. S.S. modelled electricity
generation and NEM returns. S.S. and A.A. conceptualized economic
models and aided in interpretation of energy results. A.D.K. aided
interpretation and along with R.A. and J.R. aided in food and water
methodology conceptualization. All authors contributed to manuscript
editing and revising. D.W.H., A.D.K., A.A. and R.A. acquired funding.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary
material available at https://doi.org/10.1038/s41893-025-01546-4.
Correspondence and requests for materials should be addressed to
Jacob T. Stid.
Peer review information Nature Sustainability thanks Greg
Barron-Gaord, Paul Mwebaze and the other, anonymous, reviewer(s)
for their contribution to the peer review of this work.
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