Open Access Publications

Permanent URI for this collection

Open access publications by faculty, postdocs, and graduate students in the Department of Geography and Spatial Sciences.


Recent Submissions

Now showing 1 - 5 of 26
  • Item
    Fair trade rebels : coffee production and struggles for autonomy in Chiapas
    (University Of Minnesota Press, 2019-12-10) Naylor, Lindsay
    Is fair trade really fair? Who is it for, and who gets to decide? Fair Trade Rebels addresses such questions in a new way by shifting the focus from the abstract concept of fair trade—and whether it is “working”—to the perspectives of small farmers. It examines the everyday experiences of resistance and agricultural practice among the campesinos/as of Chiapas, Mexico, who struggle for dignified livelihoods in self-declared autonomous communities in the highlands, confronting inequalities locally in what is really a global corporate agricultural chain. Based on extensive fieldwork, Fair Trade Rebels draws on stories from Chiapas that have emerged from the farmers’ interaction with both the fair-trade–certified marketplace and state violence. Here Lindsay Naylor discusses the racialized and historical backdrop of coffee production and rebel autonomy in the highlands, underscores the divergence of movements for fairer trade and the so-called alternative certified market, traces the network of such movements from the highlands and into the United States, and evaluates existing food sovereignty and diverse economic exchanges. Putting decolonial thinking in conversation with diverse economies theory, Fair Trade Rebels evaluates fair trade not by the measure of its success or failure but through a unique, place-based approach that expands our understanding of the relationship between fair trade, autonomy, and economic development.
  • Item
    Adaptation to compound climate risks: A systematic global stocktake
    (iScience, 2023-02-17) Simpson, Nicholas P.; Williams, Portia Adade; Mach, Katharine J.; Berrang-Ford, Lea; Biesbroek, Robbert; Haasnoot, Marjolijn; Segnon, Alcade C.; Campbell, Donovan; Musah-Surugu, Justice Issah; Joe, Elphin Tom; Nunbogu, Abraham Marshall; Sabour, Salma; Meyer, Andreas L.S.; Andrews, Talbot M.; Singh, Chandni; Siders, A.R.; Lawrence, Judy; van Aalst, Maarten; Trisos, Christopher H.; The Global Adaptation Mapping Initiative Team
    Highlights: • Compound climate impacts are particularly hard to adapt to • Compound vulnerabilities and exposures constrain adaptation capabilities • Inappropriate responses to climate change can lead to maladaptation • Compound impacts can have cascading effects on response options Summary: This article provides a stocktake of the adaptation literature between 2013 and 2019 to better understand how adaptation responses affect risk under the particularly challenging conditions of compound climate events. Across 39 countries, 45 response types to compound hazards display anticipatory (9%), reactive (33%), and maladaptive (41%) characteristics, as well as hard (18%) and soft (68%) limits to adaptation. Low income, food insecurity, and access to institutional resources and finance are the most prominent of 23 vulnerabilities observed to negatively affect responses. Risk for food security, health, livelihoods, and economic outputs are commonly associated risks driving responses. Narrow geographical and sectoral foci of the literature highlight important conceptual, sectoral, and geographic areas for future research to better understand the way responses shape risk. When responses are integrated within climate risk assessment and management, there is greater potential to advance the urgency of response and safeguards for the most vulnerable. Graphical abstract at:
  • Item
    Transnational agricultural land acquisitions threaten biodiversity in the Global South
    (Environmental Research Letters, 2023-02-02) Davis, Kyle Frankel; Müller, Marc F.; Rulli, Maria Cristina; Tatlhego, Mokganedi; Ali, Saleem; Baggio, Jacopo A.; Dell'Angelo, Jampel; Jung, Suhyun; Kehoe, Laura; Niles, Meredith T.; Eckert, Sandra
    Agricultural large-scale land acquisitions have been linked with enhanced deforestation and land use change. Yet the extent to which transnational agricultural large-scale land acquisitions (TALSLAs) contribute to—or merely correlate with—deforestation, and the expected biodiversity impacts of the intended land use changes across ecosystems, remains unclear. We examine 178 georeferenced TALSLA locations in 40 countries to address this gap. While forest cover within TALSLAs decreased by 17% between 2000 and 2018 and became more fragmented, the spatio-temporal patterns of deforestation varied substantially across regions. While deforestation rates within initially forested TALSLAs were 1.5 (Asia) to 2 times (Africa) higher than immediately surrounding areas, we detected no such difference in Europe and Latin America. Our findings suggest that, whereas TALSLAs may have accelerated forest loss in Asia, a different mechanism might emerge in Africa where TALSLAs target areas already experiencing elevated deforestation. Regarding biodiversity (here focused on vertebrate species), we find that nearly all (91%) studied deals will likely experience substantial losses in relative species richness (−14.1% on average within each deal)—with mixed outcomes for relative abundance—due to the intended land use transitions. We also find that 39% of TALSLAs fall at least partially within biodiversity hotspots, placing these areas at heightened risk of biodiversity loss. Taken together, these findings suggest distinct regional differences in the nature of the association between TALSLAs and forest loss and provide new evidence of TALSLAs as an emerging threat to biodiversity in the Global South.
  • Item
    Regional Sources and Seasonal Variability of Rainwater Dissolved Organic and Inorganic Nitrogen at a Mid-Atlantic, USA Coastal Site
    (Journal of Geophysical Research: Biogeosciences, 2023-02-08) Czarnecki, J. I.; Levia, D. F.; Scudlark, J. R.; Ouyang, T.; Wozniak, A. S.
    Changes in anthropogenic activities have altered the speciation and concentration of inorganic reactive nitrogen (Nr) delivered to coastal and oceanic waters with precipitation. Less is known about rainwater dissolved organic nitrogen (DON) despite its quantitative importance (>20% of Nr) and potential contributions to primary and secondary production. We document decreases in rainwater nitrogen and carbon amounts between 1994 and 2019 in Delaware, USA with the major reduction observed for nitrate (64%) reflecting emissions technology improvements. [DON] in 2019 was 55% that of 1994, though only 2 years of data are available precluding any assessment of trends. Season, airmass back trajectory (AMBT), rainfall amount, and meteorology influenced Nr amounts in 2018–2019 rain. [DON], which peaked in Summer, had different seasonal patterns than inorganic Nr and dissolved organic carbon, suggesting a biological source. Marine AMBT events showed the lowest Nr abundances. AMBTs from the southwest had the highest concentrations of Nr and DOC partially due to low rainfall amounts. Characterization of the oxidized fraction of DON revealed abundant highly unsaturated aliphatic and peptide-like formulas suggesting a combination of secondary organic, biomass burning, and biological sources. The large changes in Nr and DOC loads emphasize the dynamic nature of atmosphere to land/water fluxes due to the influence of anthropogenic processes with potential implications for coastal and oceanic water quality and ecology. Models of atmospheric deposition to watersheds and the ocean should be frequently reevaluated with current data to accurately assess inputs from changing atmospheric sources. Plain Language Summary: Despite its quantitative importance (>20% of rainwater reactive nitrogen (Nr)) and contributions to primary and secondary production in coastal and oceanic waters, temporal variability in dissolved organic nitrogen (DON) amounts and characteristics remains understudied. Here, we document reductions in rainwater nitrate (64%) and DON (55%) amounts in 2019 relative to 1994 in Delaware, USA. The nitrate reductions likely reflect improvements in anthropogenic emissions technologies. Only 2 years of DON data are available, unfortunately, precluding assessment of long-term trends. Season, airmass back trajectory (AMBT), rainfall amount, and meteorology influenced Nr amounts in 2018-2019 rain events. DON concentrations peaked in summer while inorganic Nr and dissolved organic carbon were highest in spring, suggesting a distinct biological source for DON. Marine AMBT events showed the lowest Nr abundances, and southwest AMBTs had the highest concentrations of Nr and DOC partially due to low rainfall amounts. Molecular analyses show DON to be primarily composed of compounds suggestive of biomass sources though Northwest AMBT rainwater showed evidence for the influence of anthropogenic inorganic sulfur co-emissions. These results demonstrate the need to reevaluate Nr (and DON in particular) amounts and characteristics as the patterns of anthropogenic emissions change locally, regionally, and globally. Key Points: - Mid-Atlantic rainwater reactive nitrogen and dissolved organic carbon fluxes are significantly reduced over the last 25 years - Rainwater dissolved organic nitrogen abundances are uncoupled from inorganic nitrogen and dissolved organic carbon - Dissolved organic nitrogen compositional data and peak Summer abundance suggests a biological origin
  • Item
    Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran
    (Journal of Hydrology: Regional Studies, 2022-12-01) Meydani, Amirreza; Dehghanipour, Amirhossein; Schoups, Gerrit; Tajrishy, Massoud
    Study region: This study develops the first daily runoff forecast system for Bukan reservoir in Urmia Lake basin (ULB), Iran, a region suffering from water shortages and competing water demands. Study focus: A weather forecast downscaling model is developed for downscaling large-scale raw weather forecasts of ECMWF and NCEP to small-scale spatial resolutions. Various downscaling methods are compared, including deterministic Artificial Intelligence (AI) techniques and a Bayesian Belief Network (BBN). Downscaled precipitation and temperature forecasts are then fed into a rainfall-runoff model that accounts for daily snow and soil moisture dynamics in the sub-basins upstream of Bukan reservoir. The multi-objective Particle Swarm Optimization (MOPSO) method is used to estimate hydrological model parameters by maximizing the simulation accuracy of observed river flow (NSEQ) and the logarithm of river flow (NSELogQ) in each sub-basin. New hydrological insights for the region: Results of the weather forecast downscaling model show that the accuracy of the BBN is greater than the various deterministic AI methods tested. Calibration results of the rainfall-runoff model indicate no significant trade-off between fitting daily high and low flows, with an average NSEQ and NSELogQ of 0.43 and 0.63 for the calibration period, and 0.54 and 0.57 for the validation period. The entire forecasting system was evaluated using inflow observations for years 2020 and 2021, resulting in an NSE of 0.66 for forecasting daily inflow into Bukan reservoir. The inflow forecasts can be used by policymakers and operators of the reservoir to optimize water allocation between agricultural and environmental demands in the ULB. Graphical Abstract: Available at
Copyright: Please look at individual material in order to see what the copyright and licensing terms are. Some material may be available for reuse under a Creative Commons license; other material may be the copyright of the individual author(s) or the publisher of the journal.