Physics Based Predictive Modeling for Integrated Agricultural and Examination of Socio-Economic Success Factors Associated with Agri-Urban Development

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Information about Physics Based Predictive Modeling for Integrated Agricultural and...

Published on December 22, 2016



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:, 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 + ***

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