This document summarizes a research project that aims to understand consumer behavior related to urban farming in order to identify factors for its success. The researchers conducted an online survey (N=325) to assess how consumers perceive and have knowledge of urban farming, their attitudes towards it, and the likelihood they would buy produce from or grow produce at an urban farm. The survey found that consumers associate urban farming most with food attributes and being environmentally friendly. Their subjective knowledge of urban farming is moderate. Attitudinal factors like perceived health benefits and costs positively influence purchasing and growing intentions. A bivariate ordered probit model identified these behavioral success factors. The integrated modeling system developed will help assess agricultural and urban development pathways and their socioeconomic impacts.

1. Earth System Models-3: Physics-Based Predictive Modeling for Integrated Agricultural and Urban Applications
Project Directors: Alex Mahalov, Arizona State University and Fei Chen, NCAR
Objectives
● Develop an integrated agricultural and urban modeling
system
● Characterize decadal and regional impacts associated
with agriculture/urban expansion for selected regions in
the continental US
● Examine socio-economic impacts associated with agri-
urban development including urban farms/community
gardens
● Educate next generation of interdisciplinary scientists
Approach
● Physics based predictive modeling and data
development supporting agricultural management
strategies and policy decisions at multiple scales
● Advanced modeling system includes crop modeling
capabilities embedded in a land surface Noah-
MP/biogeochemistry/hydrology model with tiling for
accommodating a mixture of crop/urban landscapes
● High resolution USDA National Agriculture Imagery
Program (NAIP) datasets are integrated in data
development
Impact
● Developed a new paradigm for studies of linked
regional agricultural and urban systems on decadal
time scales
● Assessment of agri-urban development pathways
● Created advanced physical and cyberinfrastructure to
support continued integration across disciplines
● The integrated agricultural and urban modeling system
will be released for community use
USDA-NIFA awards # 2015-67003-23508
and 2015-67003-23460; NSF # 1419593
NAIP Dataset

2. REPRESENTATIVE PUBLICATIONS in FY 2016 (from a total of 18 published papers)
Li, Mahalov and Hyde, Simulating the impacts of chronic ozone exposure on plant’s conductance and photosynthesis, and on
hydroclimate in the continental U.S., Environ. Res. Lett. 11, 114017, doi:10.1088/1748-9326/11/11/114017D-15-02, 2016.
Mahalov, Li and Hyde, Regional impacts of irrigation in Mexico and southwestern U.S. on hydrometeorological fields in the North
American Monsoon region, Journal of Hydrometeorology, American Meteorological Society, published DOI:
http://dx.doi.org/10.1175/JHM-23.1, 2016.
Li, Mahalov and Hyde, Impact of agricultural irrigation on ozone concentrations in the Central Valley of California and in the
contiguous United States based on WRF-Chem simulations. J. Agricultural and Forest Meteorology, pp. 34-49.DOI:
10.1016/j.agrformet.2016.02.004, 2016.
Shaffer, Moustaoui, Mahalov and Ruddell, A method of aggregating heterogeneous subgrid land-cover input data for multiscale
urban parameterization, Journal of Applied Meteorology and Climatology, 55, 1889-1905, 2016.
Li, Middel, Harlan, Brazel, Turner, Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona:
Combined effects of land composition and configuration and cadastral demographic—Economic factors. Remote Sens.
Environ. 174, 233–243, 2016.
Salamanca, Georgescu, Mahalov, Moustaoui, and Martilli, Citywide impacts of cool roof and rooftop solar photovoltaic deployment
on near-surface air temperature and cooling energy demand, Boundary-Layer Meteorology, doi: 10.1007/s10546-016-0160-y,
2016.

3. Feedback Loops: Agricultural irrigation affects North American monsoon(NAM) rainfall
112W 108W 104W 100W
20N
24N
28N
32N
0
0.1
0.2
0.5
1.0
1.5
2.0
2.5
3.
0
3.5
Terrainelevations(km)
AZNM
NWMX
CNMX
●The irrigated lands in the NAM region
comprise 22.67 million acres and consume
about 70.789 million acre-feet water per year.
●First time in the scientific community to
quantify how agricultural irrigation in SW US
and Mexico affects North American monsoon
using a modified WRF/Chem model.
Agricultural irrigation lands (Marked as
Blue) and urban lands (gray)
▬Regional impacts of irrigation in Mexico and southwestern
U.S. on hydrometeorological fields in the North American
Monsoon region, J. Hydrometeorology., 17,2982-2995, 2016.

4. Agricultural irrigation affects North American monsoon rainfall
Key findings:
● Irrigation modifies rainfall which
varies with location and NAM rainfall
variability.
● Irrigation increases rainfall in
eastern Arizona--western New
Mexico and in northwestern Mexico.
● Irrigation decreases rainfall in
western Arizona, along the western
slope of the SMO, and in central
Mexico.
● Irrigation modifies convective
rainfall.
-
2.
0
-
1.
0
-
0.5
-
0.
2
-
0.
1
-
0.0
1
0.01 0.
1
0.
2
0.
5
1.
0
2.
0
Rainfall (mm/d)
Total precipitation Convective rainfall
Irrigation-induced precipitation changes
(2000-2012)

5. Agricultural irrigation also affects atmospheric chemistry in the lower troposphere:
Changes of maximum 8 hr daily (DMA8) ozone concentrations ([O3])
▬Impacts of agricultural irrigation on ozone concentrations in
the Central Valley of California and in the contiguous United
States based on WRF-Chem simulations, J. Agricultural and
Forest Meteorology, 221, 34-49, 2016.
,,
-
10
-7 -4 -2 -1 -0.5 0.5 1 2 4 7 10
DMA8 [O3] differences (ppb)
Ozone changes 4-km resolution Ozone changes at 12-km
resolution
● Irrigation decreases surface
DMA8 [O3] up to 0.5-5 ppb in the
irrigated areas in California’s
Central Valley.
●Irrigation increases surface
DMA8 [O3] up to 0.5-7 ppb in
non-irrigated areas of
California’s Central Valley.
●The conclusion can be extended
to the contiguous U.S.
Key findings:

6. Effects of agricultural irrigation on atmospheric chemistry in the lower troposphere:
changes of Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Volatile Organic
Compounds (VOC)
5
10
-5
-10
-30
-50
30
50
40
20
-20
-40
[CO]changes(ppb)
-
1-
2-
4-6
-8
-
10
1
2
4
6
8
10
[NOx]changes(ppb)
-1
-2
-4
-6
-8
-10
1
2
4
6
8
10
[VOC]changes(ppb)
[CO] changes [NOx] changes [VOCs] changes
● Increases surface [CO] up to 40 ppb with an irrigated grid average of 16 ppb or
8.3%;
● Increase [VOC] up to 10 ppb with an irrigated grid average of 4.6 ppb or 21.4%; and
● Increase [NOx] up to 4 ppb with irrigated grid average of 0.72 ppb or 12.6%.
Agricultural irrigation:
Key findings:

7. Atmospheric compound (here chronic ozone) variations modify hydroclimate
through non-radiative forcing effects: A new feedback loop
● Ozone can penetrate the leaves of plants
through the stomata to:
▬ oxidize plant tissue,
▬ impair photosynthesis,
▬ affect the metabolic activity, and
▬ reduce stomatal conductance;
● The non-radiative effects of chronic
ozone exposures on surface temperature
and precipitation, both of which affect
vegetation’s transpiration and
photosynthesis as well as photochemical
reaction rates and ultimately [O3]
themselves, have first time been
investigated using a modified two-way
coupled model system.
-250
-200
-150
-100
-50
0
50
0 10 20 30 40
Cumulative ozone (ppm hr)
Acc.Transp.Diff(mm)
▬Simulating the impacts of chronic ozone
exposure on plant conductance and
photosynthesis, and on the regional hydroclimate
using modified WRF/Chem, Environmental
Research Letters, 11 (2016), 114017.
Chronic ozone exposures decrease transpiration

8. 0
1
2
3
107 08 09 10 11 12
T2changes(oC)
Year
0
1
2
3
0 3 6 9 12 15 18 21 0
NOSLO
P-O40
Temperaturechanges(oC)
GMT time
-0.1-0.2-0.5-1.0-2.0-3.0 0.1 0.2 0.5 1.0 2.0 3.0
2-m Temp. changes (oC)
Mean Temperature changes Diurnal cycle change Interannual variations
Chronic ozone exposures
● Increase surface temperatures up to 0.5-2 oC on average;
● Increase daily temperature ranges up to 0.4-1oC; and
● Result in temperature change experiencing interannual variations.
Chronic ozone variations modify temperature through non-radiative forcing effects
Key findings:

9. Chronic ozone exposures
● Decrease precipitation (mainly convective rainfall) up to 0.2-1.0 mm/d on average;
● Result in precipitation change experiencing interannual variations; and
● Change precipitation features (diurnal cycle, precipitation type).
-0.1-0.2-0.5-1.0-2.0-3.0 0.1 0.2 0.5 1.0 2.0 3.0
Changes (mm/d)
Mean precipitation changes Interannual variations
-3
-2
-1
0
1
Rainfallchanges(mm/d)
07 08 09 10 11 12
Year
Chronic ozone variations modify precipitation through non-radiative forcing effects
Key findings:

10. Summary
● A two-way, multiple-scale, and process-based multi-physics model system (including
atmospheric physics, atmospheric chemistry, biogeochemistry, land cover and land
use changes, and their interactions) is developed based on Weather Research and
Forecasting (WRF) model with Chemistry (WRF/Chem).
● The model’s performance is validated against observations from ground as well as
from remote sensing data and its improvement is documented comparing with the
model results without modifications.
● The modified model system has been applied to investigate the effects of agriculture
on hydroclimate and atmospheric chemistry, and effects of atmospheric chemistry
on agriculture and hydroclimate at regional to continental scale.
● Nonlinear Feedback Loops: interactions of agriculture (including productivity),
hydroclimate and atmospheric processes at crop field-scale.

11. High Resolution National Agriculture Imagery Program (NAIP) Datasets are
Integrated in Data Development. Example: recoding of the land-cover map
for Baltimore County, 1m resolution

12. Black lines are
city boundaries
Extent of the
yellow polygon
60 * 73 sqkm
Study area selection of the Central California, 1-m resolution classification

13.  Land architecture affects land surface
temperature (LST) of residential
parcels.
 Land-cover composition has the largest
effect on LST but land-cover
configuration is significant.
 Compact and concentrated land-covers,
foremost vegetation, improves nighttime
cooling.
 Large land-cover units of irregular
shape improve daytime cooling.
 Parcel level land architecture can be
used to mitigate the LST of residences.
Examples of the land cover of Phoenix neighborhoods (1 m) and their land surface temperatures
(LST) (6.8 m). (a) Low-level (xeric) and (b) high level (mesic) vegetated neighborhoods; (c) daytime
LST of xeric and (d) nighttime LST of mesic neighborhoods.
(a) (b)
(c) (d)

14. SUMMARY
Data Development: land identification from high resolution remote sensing imagery
• Identified vacant land for potential urban agriculture applications
over large metropolitan areas by developing an accurate, replicable
method utilizing remote sensing data and cadastral data:
• Cadastral data alone does not provide information on parcel physical
conditions and are not always correct or up-to-date;
• Remote sensing alone cannot discern parcel boundaries, is time
consuming, and has difficulty classifying some land-covers in urban areas.
USDA National Agriculture Imagery Program (NAIP) datasets are integrated in data development

15. Consumer Behavior as a
Success Factor of Urban Farming
Carola Grebitus, Co-PD
Arizona State University
Morrison School of Agribusiness

16. Research Objective
Linking consumer behavior to urban farming success
Motivation
• Increasing urban population leads to a need of raising overall food
production
• One solution: converting available land to agricultural landscapes
To make urban farming successful, consumer demand is necessary
Creating consumer demand: Consumers have to be able to
perceive urban farming as a viable source for produce

17. Research Questions
1. How do consumers
generally perceive urban
farming?
2. What is consumers
subjective knowledge re:
urban farming?
3. Do consumers hold
positive attitudes towards
urban farming?
4. How do these factors
influence whether consumers
are likely to buy produce
from an urban farm and
likely to grow
their own produce at an
urban farm?
Online survey: N=325

18. Consumers’ perception of urban farming
Free elicitation technique
“What comes into your mind when you think of
urban gardens…”
Total of 478 different concepts
• Single terms (e.g., nature)
• Whole phrases (e.g., “A place where people share something…”)
Grouped into 6 categories

19. Consumers’ perception of urban farming
Other: 8%
(e.g., good idea,
not used enough)
Point of sale: 6%
(e.g., CSAs,
farmers markets)
Environment: 15%
(e.g., earthfriendly,
sustainable)
Society: 16%
(e.g., helping &
supporting local
community)
Economy: 16%
(e.g., expensive, higher
cost; cheap/cost saving)
Food & Attributes: 38%
(e.g., organic, healthy)
Urban
farming

20. Consumers’ subjective knowledge
on urban farming
Feeling informed about …
Scale from 1=no knowledge to 5=very knowledgeable

21. Attitudes towards urban farming
Factor A:
Urban Farming is better for me
Factor B:
Urban Farming: new, fit, frugal
• Urban farming allows me to eat more fruits
and vegetables
• When going to an urban farm I spend
less money on food
• Urban farming helps me to care more
about the environment
• Because of urban farming I am more
physically active
• Urban farming helps me to learn more
about gardening
• Urban farming allows me to eat new
kinds of food
• Urban farming allows me to eat more
organic food
• Urban farming helps me make new
friends

22. Reasons that prevent or encourage
purchase of produce from urban farms
Factor 1:
Healthy individual,
economy and
environment
Factor 2:
Foods and
attributes
Factor 3:
Cost and
inconvenience
Health Food Safety Cost
Freshness Variety available Convenience
Support economy Taste Time commitment
Support environment Variety in general Distance traveled
Too much work

23. Likelihood to buy produce from urban farms
60%
N=325

24. Likelihood to participate in growing produce
at urban farms
44%
N=325

25. Bivariate ordered probit
Behavioral success factors of buying and growing at urban farms
Attitude F1:
UF healthier
Attitude F3:
Cost &
Convenience
Buying
Growing
+ ***
+ ***
Perceived
knowledge
General
positive
attitude (FA)
+ **
Gender
(F)
Age
+***
+**
Education
+***
+ **
Bivariate
Ordered
Probit
Note: ***, ** ,
1%, 5% significance level.
Attitude F2:
Food / attribute
+ **
Attitude FB:
UF: new, fit,
frugal
+ ***