Shaping Land Use Change and Ecosystem Restoration in a Water-Stressed Agricultural Landscape to Achieve Multiple Benefits

Abstract

Irrigated agriculture has grown rapidly over the last 50 years, helping food production keep pace with population growth, but also leading to significant habitat and biodiversity loss globally. Now, in some regions, land degradation and overtaxed water resources mean historical production levels may need to be reduced. We demonstrate how analytically supported planning for habitat restoration in stressed agricultural landscapes can recover biodiversity and create co-benefits during transitions to sustainability. We apply our approach in California's San Joaquin Valley where groundwater regulations are driving significant land use change. We link agricultural-economic and land use change models to generate plausible landscapes with different cropping patterns, including temporary fallowing and permanent retirement. We find that a large fraction of the reduced cultivation is met through temporary fallowing, but still estimate over 86,000 hectares of permanent retirement. We then apply systematic conservation planning to identify optimized restoration solutions that secure at least 10,000 hectares of high quality habitat for each of five representative endangered species, accounting for spatially varying opportunity costs specific to each plausible future landscape. The analyses identified consolidated areas common to all land use scenarios where restoration could be targeted to enhance habitat by utilizing land likely to be retired anyway, and by shifting some retirement from regions with low habitat value to regions with high habitat value. We also show potential co-benefits of retirement (derived from avoided nitrogen loadings and soil carbon sequestration), though these require careful consideration of additionality. Our approach provides a generalizable means to inform multi-benefit adaptation planning in response to agricultural stressors.

Full text

ORIGINAL RESEARCH
published: 31 August 2020
doi: 10.3389/fsufs.2020.00138
Frontiers in Sustainable Food Systems | www.frontiersin.org 1August 2020 | Volume 4 | Article 138
Edited by:
Yanjun Shen,
University of Chinese Academy of
Sciences, China
Reviewed by:
Kyle Frankel Davis,
University of Delaware, United States
Lindsey M. W. Yasarer,
Agricultural Research Service (USDA),
United States
*Correspondence:
Benjamin P. Bryant
bpbryant@stanford.edu
T. Rodd Kelsey
rkelsey@tnc.org
Specialty section:
This article was submitted to
Water-Smart Food Production,
a section of the journal
Frontiers in Sustainable Food Systems
Received: 19 May 2020
Accepted: 03 August 2020
Published: 31 August 2020
Citation:
Bryant BP, Kelsey TR, Vogl AL,
Wolny SA, MacEwan D, Selmants PC,
Biswas T and Butterfield HS (2020)
Shaping Land Use Change and
Ecosystem Restoration in a
Water-Stressed Agricultural
Landscape to Achieve Multiple
Benefits.
Front. Sustain. Food Syst. 4:138.
doi: 10.3389/fsufs.2020.00138
Shaping Land Use Change and
Ecosystem Restoration in a
Water-Stressed Agricultural
Landscape to Achieve Multiple
Benefits
Benjamin P. Bryant 1,2
*, T. Rodd Kelsey 3
*, Adrian L. Vogl 2, Stacie A. Wolny2,
Duncan MacEwan 4, Paul C. Selmants 5, Tanushree Biswas 3and H. Scott Butterfield 6
1Water in the West, Woods Institute for the Environment, Stanford University, Stanford, CA, United States, 2The Natural
Capital Project, Woods Institute for the Environment, Stanford University, Stanford, CA, United States, 3The Nature
Conservancy, Sacramento, CA, United States, 4ERA Economics, Davis, CA, United States, 5U.S. Geological Survey, Western
Geographic Science Center, Mountain View, CA, United States, 6The Nature Conservancy, San Francisco, CA, United States
Irrigated agriculture has grown rapidly over the last 50 years, helping food production
keep pace with population growth, but also leading to significant habitat and biodiversity
loss globally. Now, in some regions, land degradation and overtaxed water resources
mean historical production levels may need to be reduced. We demonstrate how
analytically supported planning for habitat restoration in stressed agricultural landscapes
can recover biodiversity and create co-benefits during transitions to sustainability. We
apply our approach in California’s San Joaquin Valley where groundwater regulations are
driving significant land use change. We link agricultural-economic and land use change
models to generate plausible landscapes with different cropping patterns, including
temporary fallowing and permanent retirement. We find that a large fraction of the
reduced cultivation is met through temporary fallowing, but still estimate over 86,000
hectares of permanent retirement. We then apply systematic conservation planning to
identify optimized restoration solutions that secure at least 10,000 hectares of high
quality habitat for each of five representative endangered species, accounting for spatially
varying opportunity costs specific to each plausible future landscape. The analyses
identified consolidated areas common to all land use scenarios where restoration could
be targeted to enhance habitat by utilizing land likely to be retired anyway, and by shifting
some retirement from regions with low habitat value to regions with high habitat value.
We also show potential co-benefits of retirement (derived from avoided nitrogen loadings
and soil carbon sequestration), though these require careful consideration of additionality.
Our approach provides a generalizable means to inform multi-benefit adaptation planning
in response to agricultural stressors.
Keywords: agriculture, climate adaptation, habitat restoration, land use change, spatial optimization, multi-benefit
planning, Sustainable Groundwater Management Act

Bryant et al. Shaping LUC for Multiple Benefits
1. INTRODUCTION
Agriculture covers over one third of Earth’s land surface (FAO
AQUASTAT, 2018), and while its expansion and intensification
have brought many benefits to humanity, the same factors have
had profound adverse impacts on biodiversity and ecosystem
services (Foley et al., 2005; Cardinale et al., 2012). Even as
agricultural land use is expected to continue shifting and expand
with human population growth and climate change, many
existing cultivated regions are stressed due to water scarcity, soil
degradation, and increased climatic extremes (Godfray et al.,
2010; Elliott et al., 2014; Gibbs and Salmon, 2015). These
stresses will require careful changes in landscape management
to sustain agricultural production, and in certain regions,
will necessitate retiring some lands from intensive production.
This is particularly true in regions where cultivated area has
expanded with a high dependence on irrigation. Irrigated
agriculture accounts for 70% of total freshwater use globally
(FAO AQUASTAT, 2018) and there are many parts of the Earth
where restricted water availability is making it more challenging
for society to balance multiple demands for water (Elliott et al.,
2014; Liu et al., 2017). Yet, irrigated agriculture contributes 40%
of global food production on 20% of agricultural land (FAO
AQUASTAT, 2018), indicating the importance of maintaining
resilient irrigated production for the food system—both in
terms of magnitude, and in terms of land-sparing potential.
Ultimately, irrigation capabilities will play a key role in adapting
to increased climate variability, though reliance on them will
need to be considered among many interacting factors and
stresses (Howden et al., 2007).
The need for adaptation in many such irrigated landscapes
provides an entry point for critical examination of what
sustainable agricultural landscapes can and should look like
going forward (Chartres and Noble, 2015; Damania et al.,
2017; Liu et al., 2017; Tian et al., 2018). As regions plan their
responses, the role that restoring natural land cover can play
should not be overlooked, as it holds potential to benefit people
and nature through provision of ecosystem services and by
reversing the impacts of habitat loss, especially where land
may be coming out of production anyway (Isbell et al., 2019).
Maintaining and enhancing natural corridors through protection
and restoration, as well as promoting semi-natural, multi-
functional landscapes can significantly contribute to recovering
biodiversity (Haas, 1995; Ponisio et al., 2016; Shahan et al.,
2017; Grass et al., 2019). Such diversified landscapes can also
provide tangible services for farmers (e.g., pollination and pest
control) and mitigate the negative impacts on air and water
quality that intensive agriculture can often have on surrounding
communities (Werling and Gratton, 2010; Gonthier et al., 2019;
Làzaro and Alomar, 2019; Schweiger et al., 2019). The scale of
these challenges require that we develop the actionable science
and feasible incentive structures needed to programmatically
reconfigure unsustainable agricultural landscapes to achieve
long-term social and ecological goals.
This study provides a real-world example of how systematic
conservation planning techniques (Kukkala and Moilanen, 2013)
can be applied to inform multi-benefit adaptation strategies
in stressed agricultural landscapes. By first considering the
uncertain pathways through which a “business as usual” (BAU)
land use might evolve (i.e., how it will unfold in response
to stressors, but without explicit concern for nature or for
optimizing multiple outcomes), spatial optimization can more
realistically identify promising landscape configurations that: (1)
efficiently guide the creation of restored habitat, (2) achieve
other co-benefits, and (3) ensure resource constraints are met
as land and water use are brought into balance. The potential
positive futures indicated by such analysis can then be used to
identify opportunities for collaboration between the conservation
and agricultural communities, with a goal of guiding land use
change toward achieving multiple benefits, such as recovery of
imperiled natural communities, resilient agricultural production,
and improved public health outcomes.
1.1. The San Joaquin Valley of California,
USA Provides an Exemplar Case Study of
the Need and Opportunity for
Programmatic Rebalancing of Agricultural
Landscapes
Over the past century, the Valley (hereafter SJV, Figure 1) has
been transformed into one of the largest agricultural economies
in the world. Since 1980, the SJV has supported agriculture
on ∼2 million hectares of cropland (Hanak et al., 2017), with
irrigated area expanding by nearly one million hectares in the 60
years prior (Mercer and Morgan, 1991). Most of this cropland
is irrigated and expansion since the early 20th century has been
made possible by major investments in infrastructure for storing
and transporting water, as well as overdrafting groundwater
(Hanak et al., 2017). As of 2018, the region produced crops with
a total value of $35 billion annually (California Department of
Food and Agriculture, 2018), on par with the GDP of many
countries, e.g., Paraguay and Uganda (World Bank, 2020).
This economic success has imposed costs on wildlife, human
health, and infrastructure. Agricultural expansion has been the
primary cause of biodiversity loss in the SJV; today over 35
wildlife and plant species are listed as threatened or endangered
and are now restricted to a relatively few remaining patches
of suitable habitat with some species losing up to 98% of
their habitat range (Williams et al., 1998; Stewart et al., 2019).
Agriculture in this area has also contributed significantly to
impaired air and water quality, leading to chronic human health
problems (Meng et al., 2010; Lockhart et al., 2013; Almaraz et al.,
2018). Groundwater extraction (Konikow, 2015) has resulted in
large-scale land subsidence, with parts of the Valley sinking by
over eight meters since the early 20th century, imperiling storage
capacity and key portions of the surface water infrastructure on
which the SJV depends (Faunt et al., 2016).
In response to these challenges, and amid significant
drought-driven fallowing (Melton et al., 2015), California
passed the Sustainable Groundwater Management Act (SGMA),
which, among other requirements, obligates locally governed
groundwater subbasins to develop plans that will achieve
sustainable groundwater use over the next two decades (Leahy,
2016). Most of the subbasins in the San Joaquin Valley
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Bryant et al. Shaping LUC for Multiple Benefits
FIGURE 1 | San Joaquin Valley Study Area. (A) The present day land uses, distinguishing remaining natural areas from annual and perennial crops, and including the
Diablo and Temblor ranges to the west outside the valley floor. The 10 regions shown are analysis units for our study, which correspond to subbasins designated for
coordinated groundwater management by the California Department of Water Resources (some regions group multiple subbasins into one unit for analysis purposes).
(B) Aggregate potential habitat quality across the study area for five focal species (selected based on habitat needs deemed representative of the over 35 listed plant
and animal species for the upland ecosystems in the San Joaquin Valley). Comparison of the natural area at right shows that most high quality areas serving several
species have been converted to agriculture.
are categorized as critically overdrafted, suffering the worst
effects of overdraft (CDWR, 2019). As a consequence many
of these subbasins will face the most severe groundwater
pumping restrictions that are currently being established by
local governance processes, and which are to be phased in
during a 20-years adaptation period. In the absence of basin-
wide coordination and supply augmentation, prior research has
estimated that complying with SGMA may require a reduction
in average cultivated area exceeding 300,000 hectares (725,000–
750,000 acres) over the next 20 years (Hanak et al., 2019)1.
1.2. Analytically Informed Multi-Benefit
Planning Can Improve Adaptation to
Scarcity
While it poses a great challenge, the impending transformation
in the SJV also presents an opportunity to proactively shape the
landscape in ways that not only ensure agricultural and water
1In this study, we refine this estimate with updated modeling and data, and
also distinguish temporary fallowing from likely permanent retirement, finding
significantly lower impacts, though direct comparisons of statistics associated with
the SJV are further complicated by imperfect overlap of in the study areas.
sustainability, but also to achieve many other socio-ecological
goals, such as biodiversity protection and improved human
health (Kelsey et al., 2018). However, given that achievement
of many of these objectives is determined by where things
happen on the landscape (rather than simply the aggregate
amounts of cultivation, retirement, or restoration), stakeholders
need a systematic way to integrate these objectives to inform
multi-benefit spatial planning. Here we present a multi-part
analytic approach that (1) develops estimates of business-as-
usual responses to SGMA, (2) optimizes for habitat conditional
on those uncertain BAU futures, and (3) assesses potential co-
benefits (Figure 2 and section 2).
A key element of our approach is built around recognizing
that multi-benefit planning in a stressed landscape requires
first understanding how it may evolve in the absence of
strategic shaping actions that consider non-market benefits.
This evolution is of course uncertain, but multiple plausible
BAU landscapes need to be considered in order to account
for changing spatial patterns that govern the opportunity costs
of habitat restoration, and the potential to shift cropping
patterns to accommodate habitat and agriculture simultaneously.
In recognition of this uncertainty, our analysis is explicitly
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Bryant et al. Shaping LUC for Multiple Benefits
organized to help inform engagement between conservation
actors and agricultural land managers about how habitat goals
can be achieved in ways that benefit communities in the SJV.
In some places, retirement under BAU conditions may align
well with opportunities for habitat restoration. However, in
other cases, collaboration would likely involve conservation
or government actors providing compensation to achieve
consolidated retirement of specific agricultural land for habitat—
the goal being to direct inevitable land retirement to places where
it can provide multiple benefits, while in turn avoiding retirement
in areas that are less valuable outside of agricultural use. To
help clarify the extent of coordination required, our summary
results identify whether selected components of the solution
are on land that would likely be retired under BAU conditions
anyway (“BAU-retired”) or whether they would require active
retirement and restoration in exchange for leaving other BAU-
retired lands in production. Our results also allow comparison
between the region-specific land requirements for habitat relative
to SGMA-driven fallowing and permanent retirement, which
in turn provides an indication of the potential for swapping
infrequently cultivated areas and areas for restoration. Finally, we
also estimated enhanced soil carbon sequestration and benefits
in the form of reductions in nitrogen application associated with
the transition from cultivation to restoration, which serve as
examples of co-benefits that may be tied to incentive programs,
provided appropriate additionality conditions are met.
The approach presented here integrates established methods
in an innovative way that is useful for guiding on-the-ground
action. Mathematically, our consideration of different land
use change scenarios corresponds to considering multiple cost
surfaces in systematic conservation planning, the importance
of which has been examined previously (Carwardine et al.,
2010), though does not appear to be routine. And while
others have examined direct optimization of habitat-enhancing
actions in landscapes under water stress (Bourque et al., 2019),
or examined evolution of agricultural landscapes and their
water use in the absence of constraints (Wilson et al., 2016),
simultaneously incorporating fine-scale, resource-constrained,
land use change modeling within the planning and optimization
process is important and underutilized. The inclusion of resource
constraints is necessary for realism, and while they are often
incorporated in hydrologic or economic optimization models
(Harou et al., 2009; Howitt et al., 2012), these models are typically
specified at a policy-relevant regional scale that is too coarse
for targeting restoration with spatially varying habitat potential.
Combining these approaches represents an advance that can help
create better outcomes in the SJV and be generalized to other
stressed agricultural landscapes.
2. MATERIALS AND METHODS
Below we briefly describe each step within Figure 2, with
additional detail provided in the Supplementary Information.
However, before delving into each of the modeling approaches,
we lay out key aspects of the scenario terminology used and how
they relate to each other.
2.1. Defining Multiple Scenario Dimensions
for Future Landscapes
This study involves development of scenarios along multiple
dimensions, with each dimension associated with a different
modeling step. At the highest level, we consider futures with
and without SGMA, which refer to annual average outcomes
in the long-run after SGMA would be implemented (i.e., post-
2040). We label a future with SGMA as “Business-as-Usual,”
where the assumption is that SGMA will be implemented, but
without specific concern for securing habitat2. The agricultural
production and retirement statistics for the BAU scenario are
generated at the coarse regional scale (i.e., the regions in
Figure 1) by the SWAP model (see below), with retirement
identified by comparing to a “No-SGMA” scenario also modeled
in SWAP. This latter scenario is not our focus but rather is
used instrumentally within the workflow to generate estimates
of SGMA impacts. The last SGMA scenario is a “restoration
scenario” which refers to what a post-SGMA future would look
like if habitat considerations were proactively taken into account
during SGMA implementation. These two scenarios are shown
by the dotted vertical groupings in Figure 2B.
On a second scenario dimension, the “BAU” and “restoration”
scenarios are made spatially explicit at the pixel level, and vary
in their exact spatial configuration even as aggregate regional
values are held constant. To indicate a spatial component to
the scenario, we use the term “landscape.” BAU landscapes are
created by spatializing the region-level production statistics from
SWAP according to a land use change algorithm. Restoration
landscapes are created by spatially optimizating for habitat taking
into account features of the BAU landscapes. Therefore, an
individual landscape is a combination of the “SGMA scenario”
(BAU or restoration) and the “Land use change (LUC) scenario”
—with individual LUC scenarios based on different assumptions
about what drives agricultural suitability and retirement. More
detail on each step is provided below.
2.2. Modeling Regional Impacts on
Agricultural Production and Retirement
To generate plausible BAU landscapes, we first estimated changes
in agricultural production necessary to meet groundwater
sustainability targets within regions. We apply the long-
established Statewide Agricultural Production Model (SWAP)
with updated calibration methodology and data (Howitt et al.,
2012; Mérel and Howitt, 2014), calibrated to SJV conditions and
a representative 21-years historical hydrology (with adjustments
for anticipated near-term climate change)3. The SWAP model
simulates water supply availability and economic conditions that
govern how agricultural producers are likely to respond to SGMA
for 10 different regions of the SJV. Specifically, SWAP provides
2We recognize it may be unusual to include a relatively new water sustainability
policy as part of “business-as-usual” —which often connotes lack of responsive
policy—but the focus of our paper is on the marginal effect of considering
restoration assuming SGMA is implemented.
3Calibration was conducted using actual historical water supply estimates from the
period 1974–94, but the No-SGMA and BAU SGMA futures reflect adjustments
to the hydrology that factor in 2030 climate using techniques developed for the
California Water Commission.
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Bryant et al. Shaping LUC for Multiple Benefits
FIGURE 2 | Overall modeling workflow, linking regional economic modeling, land use change, conservation planning, and co-benefits assessment. (A) (Upper) Shows
the high-level workflow in terms of questions, models, and the form of output at each step. (B) (Lower) Shows the connections across workflow at a more mechanistic
level, while highlighting the scenario logic. The paper focuses on characterizing BAU SGMA and Restoration SGMA scenarios, while recognizing uncertainty in their
exact spatial patterns (LUC scenarios).
per-region estimates of average annual cultivated acreages under
different water supply and policy scenarios, and intermediate
outputs are used to infer the levels of permanent retirement
necessary to achieve SGMA-compliant water use. SWAP also
estimates revenue associated with each crop category, which is
used to inform costs of securing land for habitat restoration in
the optimization step described farther below.
2.3. Creating Spatially Explicit BAU
Landscapes
To spatialize these changes within regions, we used a suitability-
based heuristic land use change (LUC) algorithm (National
Research Council, 2014) customized for this study. It begins
with present day cropping patterns but updates them to align
consistently with the region-specific production statistics output
by SWAP in the step above. Recognizing that drivers of landscape
change are uncertain, we developed multiple LUC scenarios
based on different weighting combinations of four different
potential drivers of agricultural suitability. These included high
level assessments of “land assets,” “land quality” (both from
Thompson and Pearce, 2018), as well as custom-developed
groundwater availability and surface water availability layers
(described in Supplementary Information). The algorithm
operates such that the lowest ag suitability pixels in each region
are most likely to be retired, and also that, in regions where
particular classes (namely perennial) expand, expansion occurs
on high quality land currently occupied by lower value annuals.
Each of the drivers that serve as input to create the ag
suitability layer is a raster normalized so that a pixel value of
1 correlates with (all else equal) high suitability for agriculture,
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Bryant et al. Shaping LUC for Multiple Benefits
and zero indicates the lowest suitability. For example, because
“land impairment” is assumed to be inversely correlated with
agricultural suitability, the highest values in the original land
impairment layer receive a zero, while the lowest values receive
a 1. We consider five LUC scenarios in total: One for equal
weighting of all four drivers, and one each where the majority
weight (75%) is on each of the four specific drivers (with
corresponding names based on the primary driver, as in
Figures 5,7). For example, the “Groundwater” LUC scenario has
75% weight on the groundwater suitability driver layer, and 8.33%
weight on each of the other three drivers.
The result is a description, for each BAU landscape, of how
much area in each pixel in the landscape is under perennial
crops, under annual crops (as well as region-specific cropping
intensities), and how much is retired. Different users may find
different scenarios more or less plausible and the workflow
allows for easily generating new scenarios based on alternate
weightings. Additional data collection and expert input could
further refine these scenarios, with region-specific differences in
drivers or more data-driven approaches based on multinomial
logit downscaling (Chakir, 2009). This step (and the subsequent
spatial optimization step) are presented at 1,080 m resolution,
which balances several considerations. While somewhat finer
resolution would be possible (habitat and some other data are
provided at 270 m), using 1,080 m pixels also serves to enforce
a minimum area worth engaging in for conservation effort. It
is also important to bear in mind that actual areas of different
land categories are tracked within each pixel, so, while the pixels
are coarse, our workflow does not assume that the entirety of
the pixel is of one particular class. This aspect significantly
mitigates potential error arising from coarse pixelization, while
also increasing the speed and decreasing memory requirements
for the land use change and optimization modeling.
2.4. Spatial Optimization to Generate
Restoration Landscapes
The next major component of our analysis involves identifying
priority areas for restoration when considering opportunity costs
to agriculture, with a focus on locations that are consistent across
multiple plausible future BAU landscapes (i.e., across multiple
LUC scenarios). We utilized systematic conservation planning
(spatial optimization) software to optimize the selection of lands
for restoration (Beyer et al., 2016; Hanson, 2020), repeating the
process for each BAU landscape and then examining areas of
overlap, both across solutions, and with areas considered likely
to be retired under business as usual. In addition to cost surfaces
derived from the BAU landscapes, the key inputs are maps from
(Stewart et al., 2019) indicating where restoring lands would
result in high quality habitat for each of five target species: San
Joaquin kit fox (Vulpes macrotis mutica), giant kangaroo rat
(Dipodomys ingens), Tipton kangaroo rat (Dipodomys nitratoides
nitratoides), blunt-nosed leopard lizard (Gambelia sila), and
San Joaquin woolly-threads (Lembertia congdoni). These species
collectively are believed to be representative of the habitat needs
of the more than 35 listed plant and animal species that occupy
the once abundant upland desert scrub and grassland habitats of
the region (Williams et al., 1998; Germano et al., 2011; Stewart
et al., 2019).
The optimization was implemented as a cost-minimization
problem in which the agricultural opportunity cost layer was
treated as the cost layer (with a boundary penalty, discussed
below), and the constraints to be achieved were minimum
areas of high quality habitat. More specifically, we required the
optimizer to find restoration solutions that secured an additional
25,000 acres (10,117 ha) of high quality habitat for each of the
species, i.e., restoration of a specific area could count toward the
target of multiple species at once, but securing extra habitat for
one species could not come at the expense of habitat area for
another species falling below the minimum area threshold. Here,
high quality habitat was taken as the top decile of the continuous
distribution of habitat quality for each species from Stewart et al.
(2019). These targets are ambitious, but are generally consistent
with existing targets for population recovery specified by the U.S.
Fish and Wildlife Service, though those goals did not consider
retirement and restoration of agricultural land (Williams et al.,
1998). The large targets also ensure a broader palette that
recognizes not all priority land will be restored. We also included
a “boundary penalty” which penalizes overall edge-length of
natural lands under the final post-restoration configuration,
so that solutions which create clusters of aggregated habitat
(whether existing natural or newly restored) are preferred
over highly pixelized solutions. A more extensive enumeration
of assumptions and constraints underlying the optimization
framework, as well as sensitivity assessment, is provided in the
Supplementary Information.
2.5. Co-benefits Assessment
We estimate changes in average annual excess nitrogen applied
by assembling crop-specific application rates and nitrogen use
efficiencies (NUE) relevant for SJV crops. The values assigned to
annual and perennial cropland in each region are area-weighted
by the 18 SWAP crop categories for each region, drawing on
multiple sources for application rates and efficiencies (see “Excess
Nitrogen Application” portion of Supplementary Information).
This provides differentiation of average NUE and excess N
applied by each region’s crop composition and cropping
intensity. Existing groundwater nitrate levels were estimated
for each SWAP region using estimates from the State Water
Resources Control Board’s Groundwater Ambient Monitoring
and Assessment Program. We also include a coarse estimate
of avoided N emissions derived from COMET-Farm runs that
were generated for use in California-tailored implementation of
COMET-Planner (Swan et al., 2015, 2018)4.
Finally, we utilize and spatially propagate a single-cell version
of the land use and carbon scenario simulator (LUCAS) model
developed for California (Sleeter et al., 2019), which was designed
to estimate the impact of land-use change on ecosystem carbon
balance. Net sequestration of soil carbon is tracked for all
relevant transitions (perennial to retired, annual to restored,
etc.—see Supplementary Information for complete list) until
4COMET is a legacy acronym that originally stood for “CarbOn Management and
Evaluation Tool.”
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Bryant et al. Shaping LUC for Multiple Benefits
FIGURE 3 | Projected distribution of land uses on present day agricultural
footprint for each region in our study area under a BAU SGMA future. Black
corresponds to permanent retirement relative to a no-SGMA future, while the
combination of average temporary fallowing and average cultivation of annuals
represents a footprint of land under annuals under BAU SGMA. Annual
croplands are generally lower return and more flexibly re-allocated than land
under perennial crops (dark green).
a time horizon of 2100. We then spatialize the single-cell
transitions under the BAU and restoration land use scenarios
by comparing to the present day land cover, and examine the
cumulative difference between the BAU and restoration scenarios
in 2100. Unlike other aspects of this modeling workflow, the
GHG assessment assumes generic “annual” and “perennial”
crop types, rather than estimating those two categories as
weighted combinations of region-specific cropping patterns. It
does, however, approximate the cropping intensity of annuals
within different regions. Lastly, because there is uncertainty
in what the most appropriate restoration end-state may be
at different sites, and also what the fate of retired lands will
be, we run the model for different restoration end states and
different retirement end states, to assess sensitivity. Restoration
end states are parameterized as grassland or as shrubland, and
retired end states are modeled as “abandoned” or as “abandoned
with discing,” a process where farmers lightly plow to prevent
undesirable flora and fauna from establishing long-term.
3. RESULTS
3.1. Aggregate Land Use Varies
Significantly by Region, With a Mix of
Permanent Retirement and Temporary
Fallowing
We estimated that ∼86,000 hectares of existing irrigated cropland
will be permanently retired by 2040 as part of economically
FIGURE 4 | Number of times each pixel was selected for retirement under
BAU SGMA across the five LUC scenarios. The figure highlights several
“hotspot” clusters where retirement is likely regardless of the key determinants
of suitability. Note that the total area identified as retired under each scenario is
smaller than the visual footprint of heatmap pixels. This is due to (1) imperfect
overlap of all five scenarios, and (2) pixels are counted as retired if any
agricultural land existing within the pixel was retired, which could only be a
fraction of the total pixel area (the fraction of the pixel retired is tracked
throughout our analysis chain).
optimal strategies to achieve the groundwater sustainability
requirements of SGMA (Table SR1, total of black “Retired” area
in Figure 3). These results rely on anticipated surface water
supplies under a projected future climate in 2030 derived from
the CalSim Water Model and local surface water supply data.
They also assume that no supplemental water supplies are
developed and delivered to these basins, but do assume that
the regions maximize their existing capacity for groundwater
recharge during wet years (Table SM1).
This retirement total represents 4% of the currently cultivated
landscape overall, though the proportion varies from 0 to
8% across regions, based on relative surface water supplies,
dependence on groundwater, as well as applied water demands of
crops. In addition, over a representative 21 years of hydrology, a
substantial area (totaling ∼540,000 ha/year on average, or 27% of
all non-retired ag land) is expected to be intermittently fallowed
in our modeled BAU scenario. Comparing with the current
agricultural footprint indicates that much of this fallowing would
happen even in the absence of SGMA, due to natural and
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Bryant et al. Shaping LUC for Multiple Benefits
FIGURE 5 | Retirement and restoration by class, region, and LUC spatial
scenario. “BAU retired in solution” refers to land identified for restoration that is
projected to be retired even in the absence of strategic coordination. “Active
Retire and Restore” refers to land identified for restoration that would
otherwise remain in production under the BAU scenario, and would require
active coordination to bring under restoration—possibly by working to prevent
retirement of lower habitat value parcels “in exchange” for accepting the
retirement of the high-priority parcel. “BAU retired, not in solution” is land that
is projected to be retired and not targeted for restoration. Each region contains
five bars corresponding to each land use change scenario, in the order
overlayed on the plot (no retirement or restoration is predicted or selected in
R1 and R2). Overall, the magnitude and relative proportion of each land use
type are generally consistent across scenarios, with modest sensitivity to the
amount and distribution of BAU-retired land in the solution across R8 and R9.
policy-driven variability in water available for agriculture, but
we find SGMA to be responsible for a reduction of annual
average cultivated area of ∼160,000 ha (Table SR1). This varies
across regions as well, with, for example, the Tule region (R8)
seeing negligible annual fallowing, but 8,500 ha of permanent
retirement, while projections show the Pleasant Valley/Kettleman
Plain region (R10) experiencing negligible permanent retirement
but a very low average annual cultivation relative to the area
retained within the agricultural footprint.
3.2. Spatial Patterns of Retirement Vary by
LUC Scenario, but Reveal Consistent
Hotspots
Considering five scenarios that reflect the uncertainties in how
land quality and water availability will drive the location of
retirement, there are strongly consistent patterns, with many
areas predicted to be retired under most or all of the scenarios
(Figure 4). Areas of concentrated retirement occur in the north-
central Kern region (R9), in the southwestern corner of the
Tule region (R8), along the eastern edge of the Westside region
(R5) and western edge of the Kings-Tulare Lake region (R6).
More variation exists within the northern regions, including
FIGURE 6 | Areas selected for restoration in order to create 10,117 ha
(25,000 acres) of new high quality habitat for each of five focal species while
minimizing additional cost to the agricultural economy. Colors simultaneously
reflect the relative frequency with which lands were selected to be a part of the
habitat solution, as well as the frequency lands were identified as likely to be
retired. Together these provide an indication of where retirement and
restoration are well-aligned (blue colors), and where shifting production may be
useful to enhance restoration while preserving production.
Modesto (R1), Turlock (R2), Merced-Chowchilla-Madera (R3),
and Delta-Mendota (R4). Greater variation within the northern
regions reflects lower correlation between drivers of retirement.
Some boundary effects emerge based on the assumption that
analysis regions will engage in no groundwater trading across
regional boundaries, which is consistent with the current political
context but may change during the 20 years leading up to SGMA
compliance. To the extent this assumption is relaxed, some
stark boundaries may be less pronounced, with more retirement
diffusing across regional borders.
3.3. Spatial Optimization Reveals
Consistent Priority Restoration Areas
The total area selected for restoration across the different
solutions was consistent across LUC scenarios, ranging from
18,700 to 19,100 hectares (Figure 5,Table SR2). This is only 37–
38% of what would be required to reach restoration targets if high
quality habitat did not overlap among the target species, which
reflects efficiency gained by the optimizer identifying parcels that
serve multiple species.
Across all scenarios, priority restoration areas were
concentrated in the southern part of the SJV (Figure 6). Overall,
the results indicate that restoration solutions consistently
identify several contiguous clusters for restoration, in addition
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Bryant et al. Shaping LUC for Multiple Benefits
to other areas that are more scenario-dependent. Restoration
was concentrated in four core areas: around Pixley National
Wildlife Refuge in the southwestern portion of the Tule region
(R8), around Kern National Wildlife Refuge in the north-central
part of the Kern region (R9), in the western Kern region,
and the northern portion of the Pleasant Valley/Kettleman
Plain (R10) region. These clusters occur where BAU scenarios
consistently indicate lower return agricultural land (retired,
or routinely fallowed) intersecting with areas that could serve
multiple species and provide connectivity to existing natural
lands, if they are restored. Out of the eight regions where
retirement was predicted to occur, only four regions featured
in restoration solutions, with amounts of BAU-retired land
selected for restoration ranging from <1% in the Merced-
Chowchilla-Madera region (R3) to between 26 and 50% in Tule
(R8), depending on LUC scenario. Only three regions (8–10) had
substantial areas selected for restoration overall, with Kern (R9)
representing the vast majority of restoration at between 10,800
and 12,700 ha, or 58–68% of the total (Figure 5).
The restoration solutions varied significantly by region in
terms of the extent to which BAU-retired land was selected for
restoration. They also varied in terms of how much prioritizing
of active restoration (in areas not retired under BAU) would
optimally be used to achieve habitat goals (Light blue vs.
dark blue in Figure 5). The importance we placed on avoiding
fragmentation (considering newly restored or existing natural
land together), and the optimizer’s attempt to capitalize on sites
that serve multiple species resulted in solutions that identify
9,700–11,400 hectares for retirement and restoration that are not
otherwise expected to be retired under BAU scenarios (“Active
Retirement and Restoration” in Figure 5 and “Unretired-Low”
or “Unretired-High” in Figure 6, see also Table SR2). This is
despite there being significant areas of unselected retired land,
which is generally lower cost to secure for habitat. However,
the existence of unutilized BAU-retired land (“BAU retired not
in solution”) suggests opportunities for proactive engagement
to shift cultivation to less valuable habitat areas, rather than
securing retirement above and beyond that imposed by SGMA
(see section 4).
3.4. Soil Carbon Sequestration and
Mitigation of Excess Applied Nitrogen Are
Modest at a Statewide Scale, but Can Be
Significant Locally
In a no-SGMA future, our modeling results indicate an average
of ∼199,600 tons of excess nitrogen (nitrogen applied but not
consumed by crops) would be applied annually across our study
area. The retirement and temporary fallowing associated with
SGMA is predicted to reduce this amount by ∼18,800 tons
annually, or 9%. This incremental change due to SGMA-driven
retirement is modest, but given nitrate and air pollution issues
in the SJV (Lockhart et al., 2013; Almaraz et al., 2018), it
may still have noticeable local effects, depending on exposure
pathways. Our results also show limited correlation between
“retire and restore” areas with areas having high existing nitrate
concentrations in groundwater. The incremental displacement
FIGURE 7 | Positive impact of restoration on soil carbon through 2100,
reported as CO2e for comparison to social costs and other GHG sources. The
results assume that 5% of the lands identified for restoration are restored each
year from 2021 to 2040, but present cumulative totals as of the year 2100.
Initial SOC refers to whether the soil carbon in 2020 was assumed to be a high
value (100 t/ha) or low (40 t/ha). Disced retirement refers to whether retired
land is routinely disturbed to prevent weed growth that could contaminate
nearby cultivated fields.
of applied nitrogen associated with targeting for restoration
is also small, 252–307 tons annually, or just a fraction of a
percent of valley-wide excess N (Table SR3). This suggests that
if concern over nitrate contamination is a priority, it must
be actively considered in the optimization, rather than treated
as a co-benefit.
Figure 7 shows the cumulative soil carbon sequestration
impact of restoration scenarios assuming an evenly phased
transition from 2021 to 2040. These are based on propagating
the dynamics shown in Figure SR1 to represent the phased
implementation, and to account for the fact that across different
LUC scenarios, restoration portfolios may involve different areas
transitioning from specific crop classes to retired or restored.
For example, one LUC scenario may involve more transition
of perennials to restoration, while another may involve more
transition of annuals, while another may prioritize more land
that would have been retired anyway. Overall, restoration would
lead to a net enhancement of soil carbon that would average
1,590,000 tCO2e for restoration to Grassland and 1,180,000
tCO2e for restoration to Shrubland, when averaged across
parametric uncertainties shown more explicitly in Figure 7.
Parametric uncertainty is significant, but in all of the forty
combinations of parameters and spatial scenarios, the impact of
a strategic retirement and restoration strategy is positive, with
the cumulative per-hectare net increase in soil carbon through
2100 ranging from 27 to 133 tCO2e (7–36 tons of actual carbon).
Importantly, the primary drivers of variation are either decision
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Bryant et al. Shaping LUC for Multiple Benefits
variables or are measurable in advance of implementation:
Variation is driven first and foremost by initial soil carbon (as
indicated by the gap between filled and unfilled points having the
same shape and color). The next most important factor is whether
restoration is to grassland or shrubland, as indicated by the
gap between colors for the same shape and fill. Least important
are the impact of discing in retirement and variation across
spatial scenarios. In the absence of other land use pressures, it
is unlikely that a majority of land would be disced over an 80-
years time horizon, but other end uses of abandoned land may
involve soil disturbance or complete loss of vegetation as well,
in which case discing can be viewed as a coarse proxy for these
other disturbances.
Because soil carbon represents only part of the overall GHG
impacts associated with agriculture, we also analyzed a fine-
scale model-generated dataset of output from the COMET-Farm
modeling tool, which speaks to nitrous oxide emissions. When
filtered based on geography representative of the SJV, the dataset
indicated an average annual avoidance of 0.43 tCO2e ha/yr in
N2O emissions over the first 10 years following conversion
of annuals to grasslands. While the sampling is not precisely
statistically representative of the restored area, over 80% of points
modeled in COMET-Farm fall between 0.08 and 0.84 tCO2e
ha/yr, with a median value of 0.32, suggesting that a 10 years
N2O-derived emissions reduction of several tons CO2e is robust,
significantly enhancing the GHG benefits of strategic retirement
and restoration beyond that of soil carbon alone.
4. DISCUSSION
Our region-level results demonstrate that reductions in
average irrigated cultivation necessary to achieve sustainable
groundwater use in the SJV will be significant, likely being met
via a combination of permanent retirement and temporary
fallowing. Notably, however, the permanent retirement required
is a relatively small fraction of the agricultural footprint (∼4%),
and the total reduction in average annual cultivated area
attributable to SGMA is also less than some previous studies
(Hanak et al., 2017, 2019). While partitioning all the sources
of the difference between this and previous studies is not
feasible here, one major driver is likely to be an improved
calibration technique that better represents farmer adaptation
to intermittently scarce water supplies. Our spatial results
demonstrate that while the location of some permanent
retirement depends on which factors turn out to be the most
influential in driving land use changes, there are hotspot clusters
that are consistent over multiple assumptions about drivers
of land use. Similarly, several areas are robustly identified as
priorities for restoration, using a mix of BAU-retired land
and actively retired land. Consideration of these anticipated
retirement patterns will play an important role in designing
the least disruptive and most cost-effective strategies for
restoring high quality habitat for target species and achieving
other co-benefits. However, achieving positive outcomes will
depend on careful coordination and implementation, including
decisions about geographic scope and constraints on water
trading. We next discuss the benefits that can be achieved by
successfully negotiating these challenges, and then elaborate
on the policy and implementation considerations needed to
address them.
4.1. Multi-Benefit Planning for Restoration
Holds Significant Potential for Expanding
and Consolidating Habitat, but Realistic
Recovery Goals and Coordinated
Protection of Natural Lands Are Key
Our results show that it would be possible to achieve ambitious
habitat restoration targets (∼19,000 ha that ensures at least
10,000 ha of high quality habitat for each focal species) within
a small number of new or expanded protected areas. These
consolidated natural and restored areas would serve a suite of
listed species on only 1% of the land currently allocated to
irrigated agriculture, using only about 20% of the land area
expected to be retired to achieve groundwater sustainability.
Although not explicitly considered in the Recovery Plan for
Upland Species of the San Joaquin Valley (Williams et al.,
1998), the large-scale creation of new or expanded protected
areas through the use of otherwise retired farmlands has the
potential to achieve species recovery (Stewart et al., 2019), though
success is by no means guaranteed, particularly if restoring
isolated islands of farmland (Lortie et al., 2018). However, a
focus on creating new restored areas contiguous with natural
areas occupied by the target species has better prospects for
success. This is especially true when restoring or connecting
to habitat already containing species that provide foundational
(e.g., shrubs; Lortie et al., 2018, 2020; Westphal et al., 2018)
or keystone (e.g., kangaroo rats; Prugh and Brashares, 2012)
species, as these are known to facilitate enhanced habitat values
and encourage additional plant and animal species occupancy.
This strategy would allow species to naturally migrate to restored
lands, avoiding active reintroduction efforts, which can be costly,
hard to permit, and often have low success rates.
While the focus of this analysis is on identifying priority
lower-return agricultural lands for strategic restoration, the
protection and proper management of existing natural habitats
across the western SJV will also be essential for species recovery.
The importance of natural lands to the long-term persistence of
SJV endemic species has been confirmed in recent rangewide
genetic studies on the endangered blunt-nosed leopard lizard
(Richmond et al., 2017) and giant kangaroo rat (Statham et al.,
2019). Intact and protected natural lands will likely become
increasingly important to species recovery as the climate changes
and brings warmer and drier weather to large portions of the
SJV (Westphal et al., 2016; Stewart et al., 2019). Importantly,
the fact that a significant fraction of retired lands were not
selected as high priority in our optimizations does not necessarily
mean they lack potential for enhancing biodiversity in the SJV.
Conservation actors should still maintain awareness of low-cost
opportunities for restoration outside our priority areas, provided
they have confidence in their habitat benefits.
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Bryant et al. Shaping LUC for Multiple Benefits
4.2. Land Use Change Modeling and
Optimization Clarify the Opportunity Space
Between Shifting Cultivation and Additional
Retirement, Each With Varying Co-benefits
Importantly, our analysis does not recommend a specific
landscape based on the optimizer results. Rather, the robust
clusters of areas selected for restoration identify a palette within
which contiguous natural landscapes can be created, using a
mix of BAU-retired land and active retirement and restoration.
While it is expected that successful restoration would involve
securing a significant fraction of the priority areas, the results
do not constitute a fully prescriptive landscape planning design
and, crucially, do not specify what should happen on other
retired or frequently fallowed lands. In particular, when active
retirement is undertaken in priority habitat areas, the retirement
involved will often free up water rights that could then be put
to several different uses, each of which varies in how benefits
are distributed between conservation goals, landowners, and the
broader agricultural community. We would expect that in most
areas, many stakeholders will place a high priority to keep as
much land in production as possible, which can be achieved
by redistributing water rights associated with lands prioritized
for active retirement and restoration. This redistribution could
result in avoiding retirement of land that would otherwise be
retired, or more intensively cultivating land that would have
been more frequently fallowed. Alternatively, with sufficient
compensation, farmers may prefer the additional reduction in
footprint due to inefficiencies at low cropping intensities (e.g.,
discing during fallow periods). In other cases, alternative land
uses, like installation of low impact solar energy facilities, will
be a viable option that can compensate landowners for lost
agricultural income (Wu et al., 2019).
In cases where retirement occurs to achieve habitat objectives,
the avoidance of externalities created by intensive agriculture
may be non-trivial. For example, improving groundwater quality
is an urgent priority in many parts of the SJV (Moore and
Matalon, 2011; Hanak et al., 2019). Application of nitrogen-based
fertilizers is the major contributor to nitrate contamination of
groundwater with significant consequences for human health
through multiple channels (Keeler et al., 2016), that have
impacted numerous communities in the SJV who depend
on shallow groundwater wells for drinking water (Lockhart
et al., 2013). Our estimates of reductions in application of
excess nitrogen across the study area are substantial and
may have meaningful benefits for communities, which would
be incrementally improved by retirement. While mechanisms
connecting fertilizer application to exposure are complex (Keeler
et al., 2016), reduced N application may come with air quality
benefits as well, via reduction in N2O and particulate matter
exposure from agriculture (Almaraz et al., 2018).
However, there were no strong correlations between areas
selected for active retirement and restoration (which would have
an associated reduction in N application) and regions where
higher nitrate contamination exist. For example, in our study
area, the Kaweah Region (R7) has well-documented high levels
of nitrate contaminated groundwater; however, no restoration
was targeted in that region based on habitat values alone. In
contrast, very large areas were targeted for restoration in Kern
(R9), where nitrate contamination is moderate (Table SR3). This
is not surprising given that selecting areas for their potential
water quality benefits was not part of the optimization. Given
the importance of this issue in the region and the potential
to take advantage of incentives and funding streams tied to
different benefits like improved water quality, planning could
be enhanced by incorporating potential for water quality
benefits (or other outcomes) as part of the spatial planning for
retirement and restoration. By spatially prioritizing restoration
in areas where potential habitat quality is high and in close
proximity to people (particularly disadvantaged communities
who have been the most impacted by impaired water quality),
it could be possible to simultaneously achieve groundwater
sustainability, support species recovery and improve
human well-being.
Finally, protection and restoration of natural habitats can
also play a role in building soil carbon and mitigating climate
change, a global issue. California has set and surpassed a goal to
reduce GHG emissions to 1990 levels by 2020 (15% below a BAU
scenario), with the additional goals to achieve emissions that are
40% below 1990 levels by 2030 (Office of the Governor, 2015)
and 80% below 1990 levels by 2050. Cameron et al. (2017) have
shown that land use management can play a significant role in
helping California achieve its emissions reductions goals. Our
study provides a specific path to a regional landscape approach
to GHG reduction: The ∼500,000–2,500,000 tCO2e of net soil
carbon sequestration correspond to a small fraction of the state’s
overall goals of 172 MMT by 2030 and 345 MMT by 2050,
though also requires a miniscule portion of land resources:
The area restored also corresponds to <120th of 1% of the
state’s land surface area. And while this number is estimated
for a longer time horizon, it also focuses only on soil carbon
sequestration, rather than a full life cycle analysis (Kendall et al.,
2015). Conservation and agricultural stakeholders may therefore
wish to pursue cap and trade funding to achieve restoration
goals given the large investments being made through California’s
carbon cap and trade program (Megerian, 2017; Taylor, 2017)
given the contribution restoration of retired lands could
make toward the state’s goals while simultaneously achieving
other benefits.
The quantitative co-benefits above are reported for the
hypothetical (and undesirable) case in which all restoration on
actively retired lands is met through net additional retirement,
i.e., there is no shifting of saved water to maintain cultivation
in other areas. This represents an extreme limiting case, both
in terms of the likelihood that it would emerge as a preferred
outcome by stakeholders across the regions, and because it does
not account for the fact that some production will inevitably
shift to other regions within or outside of the SJV. This does not
mean that these co-benefits lose their importance, but rather that
compensation for co-benefits and negotiation needs to account
for coordinated shifts within regions, as well as leakage outside
the region.
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Bryant et al. Shaping LUC for Multiple Benefits
4.3. The Ability to Successfully Negotiate
Beneficial Landscapes Requires
Coordination and Enabling Policies
Despite the opportunity SGMA-driven land use change presents
for constructive engagement between the conservation and
agricultural communities, there are significant challenges to
realizing multi-benefit outcomes while mitigating any negative
impacts to those living and working in the SJV. Consolidating
some of the agricultural land predicted for retirement into
specific, relatively large areas where the greatest habitat benefits
can be created will require strategic planning that can only
be achieved through direct coordination and cooperation
among water management agencies; each of which will have
individual mandates and mechanisms for reaching groundwater
sustainability. This level of planning and cooperation will
be even more fundamental given the need and opportunity
for simultaneously changing land use to achieve multiple
benefits that include other habitat types (e.g., temporary
wetlands that provide groundwater recharge) and renewable
energy development (Bourque et al., 2019; Hanak et al.,
2019). Consolidated and focused retirement and restoration will
not only require flexibility in trading and transfer of water
across boundaries, it will also require targeted incentives and
policies that facilitate this kind of flexibility and that defray
some of the costs associated with creating broader societal
benefits (Jack et al., 2008).
One key area for coordination outside of the water realm is
solar expansion. The potential for expanding solar in the SJV is
gaining prominence, both as an offset to livelihood challenges,
and in recognition of the need for proactive consideration to
preserve high quality habitat and high quality agricultural land
(Pearce et al., 2016; Wu et al., 2019). Our analysis does not
consider competing pressure on lands from solar expansion,
either in terms of disturbing existing natural land, or expanding
into the current agricultural footprint—nor, crucially, as an
opportunity to contribute to funding strategic land restoration.
On the one hand, uncoordinated expansion of solar may increase
the opportunity cost of securing certain lands for restoration,
though we expect this effect to be small, as the majority of
BAU-retired lands are not high priority for restoration, leaving
large aggregates of BAU-retired land available for solar. On the
other hand, and more constructively, demand for solar may
actually present an opportunity for additional coordination to
secure habitat. Environmental organizations could work with
managers of larger landholdings on the margin of profitability
(or under threat of retirement from poor water access) to blend
installation of solar, habitat, and scaled-back ag operations.
Future analytic work could also incorporate these considerations
without fundamental changes to the workflow used here.
4.4. The Analytic Framework Presented
Here Is Modular and Amenable to
Additional Uncertainty Exploration
The impact of solar is just one among many uncertainties
that may affect costs and opportunities for multi-benefit
restoration planning. Considering a wide array of uncertainties
and objectives will be an important element in developing a
robust restoration strategy. While the results presented here
focus primarily on uncertainty in BAU scenarios, supplemental
work explored other issues including definitions of natural
land, definitions of high quality habitat, variable importance
of clustering, and alternate habitat targets. While our overall
results are generally robust to the exploration of many of these
assumptions (Table SR4), some issues were infeasible to explore
within the scope of this paper (discussed with respect to each
modeling step in the Supplementary Information). However,
the workflow presented here is amenable to refinement of
individual components and assessment of sensitivity to those
refinements. High priority issues for such refinement and
sensitivity assessment include alternate land use pressures and
constraints, and more refined assessment of the opportunity
costs as perceived by the agricultural community. Both of these
could be achieved by a combination of stakeholder engagement,
elicitation of expert knowledge, or additional empirical work
to constrain predictions of fine-scale BAU land use change
consistent with the regional predictions from SWAP (cf. Chakir,
2009). Long-term, it will also be important to incorporate
additional spatially dependent objectives, such as siting for solar
(Pearce et al., 2016) and managed aquifer recharge (Hanak
et al., 2018), additional habitat or ecosystem targets, and the
impact of climate on spatial patterns of suitability for all
objectives considered.
5. CONCLUSION
Stressed agricultural landscapes exist in many areas around
the globe and are experiencing significant shifts in land use.
Aiding their transition to long-term sustainability is a key
component of meeting food systems challenges in the 21st
century. Facilitating their transition to more diverse agricultural
landscapes featuring restoration of natural lands presents an
unprecedented opportunity to reverse the impacts of natural
habitat loss on the environment and people (Isbell et al., 2019).
We show that proactive consideration of how these stressed
landscapes will evolve can create an opportunity for strategic
engagement, and demonstrate how that engagement can be
guided by a spatial analytic and optimization framework. In
the San Joaquin Valley specifically, we find that combining
economic modeling and optimization illuminates opportunities
for restoring land likely to be retired anyway, along with locations
where strategic engagement can shape retirement patterns to
benefit habitat and compensate farmers. Importantly, each
element of the approach we illustrate here can be refined in a
modular fashion based on stakeholder priorities, and the entire
workflow can be reproduced in landscapes with varying degrees
of data availability.
DATA AVAILABILITY STATEMENT
R code and harmonized spatial data used to generate spatial
land use change, spatial optimization, nitrogen co-benefit, and
carbon co-benefit propagation are included at the following Open
Science Framework archive: https://osf.io/gjtuw/. As described
in the Supplementary Information, the SWAP, COMET, and
Frontiers in Sustainable Food Systems | www.frontiersin.org 12 August 2020 | Volume 4 | Article 138

Bryant et al. Shaping LUC for Multiple Benefits
California LUCAS models are published and institutionally
maintained models. Readers are referred to published studies
(cited within) and additional details are available upon request.
AUTHOR CONTRIBUTIONS
BB and TRK coordinated the design of the research and
writing of the manuscript, with contributions from AV, SW,
HSB, and PS. DM coordinated and implemented the SWAP
agricultural economic modeling. AV and SW developed the
approaches and data inputs for nitrogen, water suitability, and
water vulnerability. TRK and HSB developed the habitat goals. BB
designed and carried out the land use change modeling, spatial
optimization, and co-benefits propagation. PS designed and
implemented LUCAS simulation models of land state transitions
and ecosystem carbon balance. TRK, TB, and BB developed the
maps and figures. All authors contributed review and edits to
the manuscript.
FUNDING
Primary project funding was provided by a 2017 Science
Catalyst Fund grant from The Nature Conservancy,
California, with majority support for Bryant provided
by the Ishiyama Foundation, and support for
Selmants provided by the USGS Biological Carbon
Sequestration Program.
ACKNOWLEDGMENTS
We would especially like to thank Jeffrey Hanson for responsive
assistance with implementing the prioritizr package, Abigail Hart
for valuable insights and reviews over the course of the project,
and Amy Swan for continued engagement on the use of COMET
outputs. This work also benefited from helpful input from Chris
Anderson, Tim Bean, Rebecca Chaplin-Kramer, Dave Marvin,
Ted Grantham, Stephen Hatchett, Peter Hawthorne, Richard
Howitt, Dave Marvin, Keith Paustian, and Joseph Stewart and
research assistance from Erin Pang. Any use of trade, firm, or
product names is for descriptive purposes only and does not
imply endorsement by the U.S. Government.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fsufs.
2020.00138/full#supplementary-material
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Bryant, Kelsey, Vogl, Wolny, MacEwan, Selmants, Biswas and
Butterfield. This is an open-access article distributed under the terms of the Creative
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