Iowa’s Arctic research dimension

1. ARCTSET:Arctic, Remote and Cold TerritoriesSocial and Environmental TrendsResearch Group@University of Northern Iowa (UNI) Iowa’s Arctic research dimension

2. Arctic Wildfires and Clime Change: A Spatial Longitudinal Analysis Using MODIS Data CORRESPONDENCE TO ENVIRONMENTAL CONDITIONS This study aims to conduct an exploratory spatiotemporal analysis to reveal spatial patterns and temporal fluctuations of wildfire events in the Arctic. Tundra wildfires have an important impact on arctic ecosystems as they can substantially alter the amount of biomass and animal abundance in affected areas. The knowledge base about tundra wildfires is limited. Most fires take place in remote areas and remain unmonitored from the ground or air. This study uses MODIS-derived active fire data to analyze spatial and temporal patterns of tundra wildfires between 2004 and 2008. The dataset incorporates locations of active fire events and estimates of fire radiated power (FRP). In 2004-2008 MODIS sensors registered over 3,000 fire events. The largest number of fire events was recorded in 2005. The wildfires exhibit clear seasonality determined by seasonal changes in tundra landscapes with most fires occurring in July and August. We observed inter-year fluctuations when a fire season either started earlier (in June) or lasted longer (in to September). The wildfires demonstrate a tendency to cluster, although year-to-year locations of clusters vary. Wildfires concentrated in Alaska and in Northwestern and Northeastern Russia. This is also true for the intensity of fires: in the five-year period the FRP values in some areas exhibited considerable spatial autocorrelation. To analyze possible factors that determine spatiotemporal variation of arctic wildfires occurrence and intensity, we analyzed fire events in respect to geographic location (latitude/longitude), climate parameters, vegetation types and proximity to points of human-caused disturbance (settlements, roads, pipelines, oil wells, etc.). The results clearly indicate the relationship between vegetation types and occurrences and intensity of wildfires: areas with larger amounts of combustible biomass and longer warm periods having a greater number and more intensive fires. We were unable to detect a clear relationship between wildfire locations and elements of anthropogenic disturbance. Results: Arctic fires spatial and seasonal distribution Correspondence with climatic parameters Research Objectives • Identify spatial (distribution and clustering) and temporal patterns (seasonal and multiyear) of arctic wildfire events and their intensity characteristics • Analyze relationships between wildfire occurrence and intensity and environmental conditions Total Fires By Year And Month Average FRP by Year and Month Data Number of Fires by Region • Wildfire data [FIRMS]: • Fire events: detected fire occurrences (confidence >= 50) • Fire Radiative Power (FRP):a measure of radiant heat output of detected fires in MegaWatts (MW) derived from MODIS Data Processing System (MODAPS) Collection 5.1 Active Fire Products • Other data: • Climate variables [U Delaware] • Vegetation and bioclimatic variables [CAVM] • Human-caused disturbance Clustering: Ripley’s K Function SPATIAL CLUSTERING, HOT-SPOTS Summary Hot Spot Analysis LISA Analysis • Spatiotemporal characteristics of wildfires: • Seasonality with most fires occurring in July and August. In some years a fire season either started earlier (in June) or lasted longer (in to September). • Multiyear geographical consistency: concentrated in Alaska, NE and NW Russia. More intensive and frequent fires are concentrated in Alaska and NE Russia. • Clustering and autocorrelation in intensity characteristics: tend to form localized isolated clusters, often of high intensity. • Relationships with environmental factors: • Both fire occurrences and intensity are positively associated with temperatures in spring, and intensity with temperatures in May and August. • More intensive fires correspond to higher temperatures in August and September. • Fire intensity declines with higher precipitation in August, September and winter. • Areas with more combustible biomass have a greater number and more intensive wildfires. • No clear relationship between wildfire locations and anthropogenic disturbance. Methodology • Exploratory Analysis: • Time series analysis: character and fluctuations in seasonality and multiyear patterns • Analysis of spatial distribution : evidence of spatial clustering (NN, Ripley’s K), spatial autocorrelation and fire hot-spots (Moran’s I , Geary’s C, LISA) • Explanatory Analysis: (correlation analysis using wildfire environmental factors/drivers and grid and zonal aggregates of fire occurrences and intensity) • Spatial correspondence with temperature and precipitation • Spatial correspondence with vegetation types and biomass • Spatial correspondence with human-induced disturbances

4. Creative Arctic: Understanding the Role of Creative Capital in the Circumpolar North Andrey N. Petrov, University of Northern Iowa, USA RESULTS ABSTRACT The vast majority of studies in economic geography of talent and creativity have focused on large metropolitan areas and core regions. However, I argue that the creative capital is an equally necessary factor (an agent of economic transformation and revitalization) in the northern frontier. This theoretical account serves as the basis for the empirical study into the economic geography of talent and creative capital in the Canadian North. The paper advances the two-ring-four-sector approach to define the creative class structure. It extends the creative capital metrics to measure four ‘sectors’ of the creative class: scientists (“talent”), leaders, entrepreneurs and bohemia. The empirical part of the paper applies the extended creative class metrics at two different scales. The findings for 288 Canadian regions suggest that the geographic distribution of the creative capital is uneven and heavily clustered in major urban centers. However, some frontier regions appear to perform exceptionally well in all rankings. The in-depth analysis of 34 communities in the Canadian North identifies creative clusters in economically, geographically and politically privileged communities that serve as creative ‘hot spots.’ Thus, contrary to the metropolitan bias, these results indicate that northern communities are not ‘hopeless places’ fully deprived of the creative capital. Creative ‘hot spots’ in the Canadian North exist, and could become the centers of regional reinvention, if appropriate policies are introduced in support. ‘Talent’ in the North • RESEARCH OBJECTIVES • Rethink and revise the creative capital approach to economic development to make it applicable in the North • Develop creative capital metrics appropriate for northern communities • Conduct an exploratory analysis of the creative capital in the Canadian North Creative capital ranking of northern communities METHODOLOGY Develop and test creative capital metrics Creative capital DIVERSITY OPENNESS • CONCLUSIONS • Creative capital (CC) is pivotal for the economic path-creation in northern regions • Most CC-economic development relationships found in the metropolis are maintained in the North • The nature and composition of the creative capital in the North is different, so are its interactions with other forms of capital, particularly social and civic. • Direct application of Florida’s scripts to peripheral communities does not appear appropriate • The four-sector approach provides a more complete and sophisticated understanding of the creative capital, It is more accurate for the periphery • Other attractiveness factors must be considered: relation with Aboriginality, role of women in the community • CC structure: evidence of both intergroup clustering and disproportions. The North most seriously lacks the entrepreneurship and leadership components of the CC • In the North there were localized concentrations of the creative class, included Yellowknife, Whitehorse, Iqaluit, Inuvik, Fort Smith, Smithers TOLERANCE ABORIGINA-LITY RESULTS Test dependencies between CC, attractiveness and specialization attractiveness attractiveness metrIcs metrIcs MORE http://www.uni.edu/apetrov/creativearctic.html

5. Coping with ‘ethanol boom’:understandingagricultural systems’ response in America’s Heartland Andrey N. Petrov, Matthew Voss & Ramanatan Sugumaran University of Northern Iowa This research is NOT funded by NSF Iowa EPSCoR

6. Iowa NSF EPSCoR • Harnessing Energy Flows in the Biosphere to Build Sustainable Energy Systems • IA is 1st in ethanol production (26%) • IA is 4th in wind energy generation • Alternative energy systems • Energy resources • Energy generation and transportation • Pending at NSF

7. Background • Much is written on energy, ecological, economic, industrial, climate impacts of increased ethanol production • Little known on coping strategies/responses in agriculture-based socio-ecological systems in the USA • Need for a detailed spatially-explicit analysis of ethanol boom implications and responses in America’s heartland. • Iowa is the main producer of corn-based ethanol

11. Research Tasks • Analyze the relationship between ethanol production and land cover change [2006-07] • Analyze the effects of ethanol on the Conservation Reserve Program lands and other vulnerable ecosystems • Analyze the effect of ethanol on long term sustainability of agriculture: • Utilization and preservation of land resources • Maintaining crop diversity • Possible impacts on soil fertility and fertilizer use

12. Working hypotheses • (1) Corn production could have displaced other crops, possibly causing a decline in crop diversity and redistribution of croplands to areas less suitable for growing crops • (2) Increases in corn production and acreage have been largely achieved by altering crop rotation patterns with more severe rotation deviations prevalent on most fertile and productive lands. • (3) Crop production expanded to Conservation Reserve Program lands and other vulnerable and underrepresented ecosystems (such as wetlands and wooded areas). • (4) Proximity of ethanol plants per se does not cause corn acreage growth in particular areas.

13. Data • NASS crop acreage and harvest statistics (2000-08) • NASS Cropland Data Layer (2000-08) • SSURGO (Soil Survey) soil data; CSR – corn Suitability Rating • FSA Crop Reserve Program data (CRP lands)

14. Data pre-processing & Issues • CDL is based on different imagery - need to resample to make comparable • Most comparable CDLs: 2005, 2006 and 2007 (same imagery source) • CDL accuracy 95% for most large crops, but only 50% for some smaller crops • Buffer ethanol plants (15 and 30 miles); separate models run for 2003, 2004 and 2007

15. Measuring crop rotation • Corn-soybeans and corn-corn-soybeans are accepted rotation patterns • Deviation: corn-corn-corn… (corn at least for three consecutive years) • 2000-2002 to 2005-2007

16. Land quality data

17. Methods • Land cover change detection (ERDAS, ArcGIS) • “expected” (Markovian probabilities) vs. actual crop acreage (Idrisi) • Crop rotation estimates • Compare crop allocation, rotation deviations and quality of land (CSR) • Overlay with CRP lands • Estimate corn increase in proximity and away from ethanol plants

18. Results

19. Increase in corn acreage -land under corn grew 12.6%, -deviated from “expected” 17% -under soybeans dropped 15% -switch in trend comparedto the 1990s.

21. Displacement of ‘other’ crops and land cover types; decrease in crop diversity Land cover types replaced by corn the following season

22. Deviation from normal rotation cycles

23. Corn x 3 seasons in a row 2004-06 2003-05 Most rotation deviations are on most fertile soils 2005-07

24. Corn and land quality • % corn planted on low CSR decreased from 34% to 29%. • cornfields area on high CSR soil increased from 66% to 71% • These data give no evidence that corn was pushed to lower quality or marginal lands. • An increasing area of more productive lands was allocated for growing corn. • This trend reflects the tendency to address the demand to increase corn planted areas by planting the most valuable crop (corn) on most suitable soils  overuse of most fertile land • 2007 season – highest ever use of fertilizers

26. CRP lands and the boom • CRP declined in 2005-2006; 2007-2009 2007 2008 • Acres eligible: 500,000 330,000 • Acres withdrawn in Iowa (%) 28 33 • Acres withdrawn in USA(%) 15 19 • Termination of CRP contracts at the peak of the ‘boom’ in 2007 and subsequent 2008 was one of the reasons of the 13.5% CRP acreage decline in 2007-2009

27. Year Near plants Not Near plants

28. Concluding remarks: Implications of ethanol boom • Overuse of primary lands, highest-quality soils • Rotation deviation and increased use of fertilizers • Displacement of secondary crops to less productive soils • Decrease in crop diversity • Withdrawal from CRP, but limited (30%) • Little relationship between location of ethanol plants and corn acreage expansion • Most coping practices are unsustainable

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