USDA iilll United :States Department of Agriicuiture
Economic Research Service 1,·11·H·.ers. tl.\d,1 .g,11 ·
Linking USGS Water Use Data to Detailed Industries for Environmental Input-Output
Modeling of the U.S. Food System Sarah Rehkamp, Patrick Canning, and Catherine Birney
2018 FCSM Research and Policy Conference March 9, 2018
The views expressed are those of the authors and should not be attributed to the Economic Research Service or USDA
USDA Economic Research Service ~ iiiillll I\ . H. H ·• l' f .\ . 11.H I". g {)I •
Motivation for studying water
• Water is a finite natural resource and primary input in the U.S. food system.
• Water faces stress due to:
– Climate change
– Population growth
– Dietary changes
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ro "C
400,000
350,000
300,000
250,000
~ 200,000 0.0 2
150,000
100,000
50,000
0 1995 2000 2005
- Aquaculture - Domestic - Irrigation - Industrial
- Mining - Thermoelectric - Public Supply ~ Population
USDA Economic Research Service iiiillll I\ . H' H ·. l' f .\ • 11.H I". g OI '
320,000
310,000
300,000
~ "C
290,000 C: ro Vl .... ::::, 0
..c: 280,000 ..:!::..
C: .Q +-' .!!1
270,000 ::::, Cl. 0 c..
260,000
250,000
240,000 2010
- Livestock
Water withdrawals 1995-2010
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Source: Authors’ calculations based on USGS data
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Motivation for linking data sources
• Environmental input-output (EIO) model
– Allows for measurement of direct and indirect water
– Used widely in the literature for resource assessment
• Answer interesting policy questions within an economic systems framework
– Rehkamp and Canning (2018) study water use in the U.S. food system using EIO
– We are now expanding to a multi-year analysis
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Farm Inputs
Water is used along all stages of the supply chain
Crop Foodservice Production
Processing •
Livestock Food Production Retailing
Household Consumption
Packaging Energy Transportation Wholesaling
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Source: Lori Fields, USDA-ERS
- - -
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Objective
• Link 8 broad water use categories from USGS to 344 narrower sectors in the EIO model
Water use categories
NAICS based industries
EIO sectors
NAICS based industries
NAICS based industries
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fR gi t r d lrrigati 11
Ue U 07 11 Ill br ka
, I 11111 h1 1 ut
Co ~or
I II llu,l, ·I,
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Source: University of Nebraska (2018)
-
USDA Economic Research Service iiiillll I\ . H. H ·• l' f .\ . 11.H I". g {)I •
D
Allocation metrics
Aquaculture Industrial Irrigation Livestock Mining Public Supply
Thermo electric
Disaggregation not necessary
Employment data from
County Business Patterns
Employment data from
County Business Patterns
Irrigated acreage in
county from Census of
Agriculture multiplied
by state irrigation
rate by crop from FRIS
Livestock inventory
from Census of
Agriculture and water
use coefficients by livestock from USGS
Public water outlays from IO model
Disaggregation not necessary
Note: Net public supply is allocated and
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domestic water withdrawals allocated to households
\
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y }
I j , ,
Irrigation allocation
𝑊𝑊𝑖𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛,𝑔,𝑛 = 𝑊𝑊𝑖𝑟𝑟𝑖𝑔𝑎𝑡𝑖𝑜𝑛,𝑔 x [𝐴𝑐𝑟𝑒𝑠𝑔,𝑛 𝑥 𝐼𝑟𝑟𝑅𝑎𝑡𝑒𝑔,𝑛]
σ𝑔,𝑛[𝐴𝑐𝑟𝑒𝑠𝑔,𝑛 𝑥 𝐼𝑟𝑟𝑅𝑎𝑡𝑒𝑔,𝑛]
Source data from USGS Share of n
based on allocation
metric
where WW is water withdrawals g is geographical index n is commodity index Acres is irrigated acres harvested (Census of Agriculture) IrrRate is irrigation rate (Farm and Ranch Irrigation Survey)
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l ____ J y
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Livestock allocation
𝑊𝑊𝑙𝑖𝑣𝑒𝑠𝑡𝑜𝑐𝑘,𝑔,𝑛 = 𝑊𝑊𝑙𝑖𝑣𝑒𝑠𝑡𝑜𝑐𝑘,𝑔 x σ𝑔,𝑛[𝐼𝑛𝑣𝑔,𝑛 𝑥 𝑊𝑈𝐶𝑛]
Source data
[𝐼𝑛𝑣𝑔,𝑛 𝑥 𝑊𝑈𝐶𝑛]
Share of n from USGS based on allocation
metric
where WW is water withdrawals g is geographical index n is commodity index Inv is livestock inventory (Census of Agriculture) WUC is water use coefficient (USGS)
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1· m111 I: :J. n..ig ,1 ,
s ject to
2 L 3
4 L 5 V
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,g,r r · H r§ , r .
, ,.
r ~ ., r r ~ ~
r 0
Constrained maximum-likelihood estimation for data suppressions
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Commodity constraint peaches < non-citrus fruit
Geography constraint county < state
Row constraint bearing age acres < total
Source: Canning (2013)
Zero variance of published statistic
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Livestock example
• Use geographical and commodity constraints • Also inform the model by number of farms by inventory range
.. -··-··--·--··-·-··-··--·-
100
---·-··-·· .. -
ventory a d Sa es: 2007 and 2002 - Con. J
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Source: Census of Agriculture (2007)
- - -
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Linking water to industries
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Also aggregate up geographically for EIO analysis.
Water use categories
Livestock
NAICS based industries
Beef cattle
EIO sectors Dairy and beef cattle
NAICS based industries
Dairy cattle
NAICS based industries
Other cattle
Crop production
Household
Energy services
Livestock production -
Processing & paclkagi ng I
Distribut ion & marketing I
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0
mi l lion liters per day
U.S. food system uses 28% of total water withdrawals, 2005
369 billion liters of water = 148 million Olympic-sized swimming pools
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Source: Rehkamp & Canning (2018)
poultry, fish, and mixtures
Grain products
Sugars, sweets, and beverages
Vegetables I Fruits I
Milk and milk products
Legumes, nuts, and seeds
Eggs and egg products
Fats, oils, and salad dressings I 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000
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Million liters per day
■ Crop production
■ Livestock production
■ Processing & packaging
■ Distribution & marketing
■ Energy services
■ Household_diet-related
Meats in current American diet use the most water
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Source: Rehkamp & Canning (2018)
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Thank You!
Contact Information:
Sarah Rehkamp
202-694-5584
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References
• Canning, P. 2013. “Maximum-Likelihood Estimates of a US Multiregional Household Expenditure System,” Econ Systems Research, Vol 25(2): June. pp.245-64
• Rehkamp, S. and Canning, P. 2018. “Measuring Embodied Blue Water in American Diets: An EIO Supply Chain Approach,” Ecological Economics, Vol 147: May. pp.179-188
• University of Nebraska. 2018. “Location of Irrigation Wells in Nebraska,” https://water.unl.edu/cropswater/newellsmap
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