Integrated modeling example: Cost-Effective Targeting of Conservation

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Integrated modeling example:
Cost-Effective Targeting of Conservation
Investments to Reduce the Northern Gulf of
Mexico Hypoxic Zone
Sergey Rabotyagov
School of Environmental and Forest Sciences
University of Washington
This research was supported by the National Science Foundation, Dynamics of Coupled Natural and Human Systems Program,
award number DEB-1010258, as well as two regional collaborative projects supported by the USDA-NIFA, award numbers
2011-68002-30190 and 2011-68005-30411.
Outline
I.
Simulation-optimization system to search for
cost-effective Gulf hypoxia reduction strategies
II. Updated results incorporating notion of
robustness and including agricultural land
retirement in suite of optimization options
III. Gaps and limitations encountered
http://water.epa.gov/type/watersheds/named/msbasin/index.cfm
http://voices.nationalgeographic.com/files/2014/04/gulf_of_mexico_hypoxiazone_NOAA-highres.jpg
Main question:
Where should we target agricultural conservation efforts
geographically to most cost-effectively achieve
reductions in hypoxia?
• Need to account for heterogeneity in
– Effectiveness of conservation efforts
– Costs of conservation efforts
– Existing conservation efforts (baseline)
• Rely on existing conservation technology
with proven local benefits (erosion control,
soil productivity)
https://www.leopold.iastate.edu/sites/default/files/imagecache/250pxwide/pubs-and-papers/2002-03stewards-our-streams-maintenance-riparian-buffers.jpg
Simulation
Simulation
Within-watershed conservation action scenarios
• Using multiple data sources including large scale farmer surveys,
classified cropland by severity of lack of conservation (“need”)
– Developed site-appropriate conservation scenarios
– Designed to maintain levels of crop production
• A targeted (although not necessarily optimized with respect
to cost-efficiency) allocation of working conservation
practices within 8-digit watersheds
• Retirement of all cropland acres from agriculture and
modeling the establishment of perennial grassland available
as an option
– We use land retirement as another “conservation option”
4 working land HUC8-level scenarios
treat
• Erosion Control : Critical or All
needed acreage
terraces on high slopes, contour or
strip cropping on all, buffers near
waterways, filter strips elsewhere
– “practices-low” (ECC) and “practiceshigh” (ECA)
treat
• + Nutrient Management: Critical or
All Acreage
erosion control + adjusted rate, form,
timing, and method of application
– “Practices+Fertilizer-low” (ENMC)
and Practices+Fertilizer-high”
(ENMA)
• All based on CEAP-NRI surveys
treat treat
treat
treat
treat
Montana
North Dakota
Michigan
Minnesota
Idaho
Wisconsin
South Dakota
New York
Michigan
Wyoming
Nevada
Iowa
Pennsylvania
Nebraska
Ohio
Utah
Maryland
Indiana
Illinois
West Virginia
Colorado
Kansas
Virginia
Missouri
Kentucky
North Carolina
Tennessee
Oklahoma
Arizona
Arkansas
New Mexico
South Carolina
Alabama
Mississippi
Legend
Georgia
Texas
USA Major Rivers
Louisiana
states
CEAP-GA HUC8
Florida
HUC-level ECC costs
$0.00 - $3,197,862.50
$3,197,862.51 - $9,470,336.07
$9,470,336.08 - $18,270,243.02
$18,270,243.03 - $30,513,664.87
$30,513,664.88 - $55,438,588.17
Montana
Montana
North Dakota
North Dakota
Michigan
Minnesota
Michigan
Minnesota
Idaho
Idaho
Iowa
New York
Michigan
New York
Michigan
Nevada
Wisconsin
South Dakota
Wisconsin
South Dakota
Wyoming
Wyoming
Nevada
Pennsylvania
Iowa
Nebraska
Pennsylvania
Nebraska
Ohio
Utah
Illinois
Ohio
Maryland
Indiana
Utah
Illinois
West Virginia
Colorado
Kansas
West Virginia
Colorado
Virginia
Missouri
Maryland
Indiana
Kentucky
Kansas
Virginia
Missouri
Kentucky
North Carolina
Tennessee
North Carolina
Oklahoma
Arizona
Tennessee
Arkansas
New Mexico
South Carolina
Oklahoma
Arizona
Mississippi
Legend
USA Major Rivers
Alabama
HUC-level ENMA costs
$0.00 - $11,396,062.59
Legend
Florida
states
Georgia
Texas
Louisiana
USA Major Rivers
CEAP-GA HUC8
$32,098,813.06 - $58,834,730.13
Huc-level land retirement costs
$97,653,801.03 - $180,903,654.81
Alabama
Louisiana
$11,396,062.60 - $32,098,813.05
$58,834,730.14 - $97,653,801.02
South Carolina
Mississippi
Texas
states
CEAP-GA HUC8
Arkansas
New Mexico
Georgia
$0.00 - $37,665,879.94
$37,665,879.95 - $109,496,833.06
$109,496,833.07 - $223,950,792.05
$223,950,792.06 - $378,861,444.64
$378,861,444.65 - $730,160,030.74
Florida
ECC
ECA
ENMC
ENMA
Simulation-Optimization
Optimization heuristic
• Wish to min   , ℎ(()

–  is candidate solution (spatial scenario)
–  ∈ , , , , ,  
– 557 modeled cropland watersheds in five major basins
– 6 options for each
– 6557 possible scenarios
• Evolutionary algorithms are methods to explore this
search space
Evolutionary Algorithm: intuition
1. Start with a set of candidate
MARB spatial configurations,
create an initial population
(each represents a watershed
configuration)
Cost
2. Pareto-compare solutions,
keep non-dominated ones
3. Use ‘survivors’ for ‘breeding’
and introduce random
mutations, iterate
4. Stop when satisfied, obtain
the Pareto frontier in the
objective (Cost, Hypoxia)
space
Hypoxia
Tradeoff Frontier
Solution for expected goal attainment using working land options only
• Recall even ENMA is targeted treatments
• 18% of CEAP cropland selected for treatment
Introducing land retirement and a
measure of robustness
• Working land only options constrain the
effectiveness of hypoxia reductions
– even “all ENMA” scenario has at least 25% chance
of not meeting the goal
• We also may worry that solutions evolved
under a particular weather (fitness landscape)
may not be efficient under a new set of
weather
Simulated variability due to weather
• Empirical 90% CI for 5,000 km2 is (8.7,10300)
• Land retirement and robust optimization included
• Dynamically “challenge” solutions with new weather
situation and discard dominated scenarios
• More land retirement drastically shrinks
variability (but gets very costly)
Montana
North Dakota
Michigan
Minnesota
Idaho
Wisconsin
South Dakota
New York
Michigan
Wyoming
Nevada
Iowa
Pennsylvania
Nebraska
Ohio
Utah
Illinois
Maryland
Indiana
West Virginia
Colorado
Kansas
Virginia
Missouri
Kentucky
North Carolina
Tennessee
Oklahoma
Arizona
Arkansas
New Mexico
South Carolina
Mississippi
Legend
states
Alabama
Georgia
Texas
Louisiana
USA Major Rivers
CEAP-GA HUC8
Florida
Huc-level land retirement costs
$0.00 - $37,665,879.94
$37,665,879.95 - $109,496,833.06
$109,496,833.07 - $223,950,792.05
$223,950,792.06 - $378,861,444.64
$378,861,444.65 - $730,160,030.74
• With land retirement included,
fewer (38 million versus 44
million acres) of cropland needs
to be treated
• 6.6 million acres in perennial
grass
Caveats and conclusions
1. Many uncertainties remain (e.g. statistical
hypoxia model ignores salinity dynamics)
2. Control of hypoxia via nutrient reductions is
likely going to be effective but is likely subject to
very large variability
3. Hypoxia targeting ignores local nutrient and soil
conservation impacts
4. Results dependent on modeled suite of
conservation actions and on fertilizer reductions
5. Can (should?) be implemented?
Some gaps
• Mismatch between the data on the “natural side” – e.g.,
have crop choice information at very fine scale in the
Cropland Data Layers—and “economic side”
– Rely on costs and returns data on county level (Ji and
Rabotyagov, 2015; Wang, Ortiz-Bobea, Chonoabayashi, 2015)
– Lack recent information on observed choices of conservation
practices (Fleming, Lichtenberg, Newburn 2015 show what can
be done when data is available)
• Conservation tech is evolving—challenges in modeling
new/sparsely adopted practices
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