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Public agencies and private enterprises increasingly desire to achieve ecosystem service outcomes in agricultural systems, but are limited by perceived conflicts between economic and ecosystem service goals and a lack of tools enabling effective operational management. Here we use Iowa-an agriculturally homogeneous state representative of the Maize Belt-to demonstrate an economic rationale for cropland diversification at the subfield scale. We used a novel computational framework that integrates disparate but publicly available data to map ∼3.3 million unique potential management polygons (9.3 Mha) and reveal subfield opportunities to increase overall field profitability. We analyzed subfield profitability for maize/soybean fields during 2010-2013-four of the most profitable years in recent history-and projected results for 2015. While cropland operating at a loss of US$ 250 ha −1 or more was negligible between 2010 and 2013 at 18 000-190 000 ha (<2% of row-crop land), the extent of highly unprofitable land increased to 2.5 Mha, or 27% of row-crop land, in the 2015 projection. Aggregation of these areas to the township level revealed 'hotspots' for potential management change in Western, Central, and Northeast Iowa. In these least profitable areas, incorporating conservation management that breaks even (e.g., planting low-input perennials), into low-yielding portions of fields could increase overall cropland profitability by 80%. This approach is applicable to the broader region and differs substantially from the status quo of 'top-down' land management for conservation by harnessing private interest to align profitability with the production of ecosystem services.

Teaching sustainability concepts to multidisciplinary classes of engineering students is challenging due to their diverse background and discipline-specific skill set and the interdisciplinary nature of the sustainability issues at stake. The present study was conducted to understand the challenges and identify opportunities for improving teaching and learning of sustainability in higher education institutions. The case study used for data collection was the Sustainable Engineering and International Development course taught at Iowa State University since 2005. We assessed the students' course content knowledge before and after the course and their perceptions about the overall course, content, and instruction methods. A mixed methods approach consisting of qualitative (focus groups) and quantitative (survey and pre-and postassessment) techniques was used for the study. Quantitative data were analyzed using descriptive statistics, explanatory analysis, and multinomial logistic regression. Thematic analysis was used to evaluate the qualitative data. The difficulty level of the pre-and post-assessment was low. Application-based questions could be included in the assessment to test beyond the levels of mere comprehension and memorization. Student perceptions about module usefulness exhibited year-to-year variation, which was attributed to the diversity of students enrolled in the course each year. Barriers to learning in a multidisciplinary engineering course included student difficulties working in multidisciplinary teams, course organization, and limited student engagement in

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Geographic Information System (GIS) tools have been used to strategically locate bioenergy facilities and optimize the relationship between biomass supply and demand, aiming to minimize overall fuel production costs. Microalgae, also termed third generation bioenergy feedstocks, are discussed for their potential to meet future energy demands. This study reviews literature on GIS applications to locate algae cultivation sites and estimate algae biofuel potential. To highlight the diversity of results, a quantitative comparison for the US studies is presented. We found two major assumptions that primarily limited the algae biofuel production potential estimates: (1) the production technology (open pond or photobioreactor), and (2) the number and type of resources considered, such as land type, CO 2 , water source, water quality, etc. All studies used binary (a location is either unsuitable or suitable) suitability models to determine areas for algae production. Most studies considered water, land, and CO 2 resources, while some also accounted for infrastructure, soil properties, and work force requirements. We found that potential cultivation area in the USA is most sensitive to the constraints of CO 2 availability and land cost. This review explains the wide range of algal biofuel potential estimates (from 0.09 to over 600 billion L yr −1 ) by identifying underlying assumptions, methodologies, and data. The highly variable outputs indicate the need for a comprehensive analysis of different criteria individually and in combination to estimate realistic biofuel potential. The results suggest that with models becoming increasingly detailed in considering resources and conversion/production technologies, further decrease in estimated theoretical production potential is expected under available technology.

Because of rising fuel prices and increasing energy demand, bioethanol has been recognized as an important future renewable energy source. The goals and mandates developed for renewable fuel production will require construction of several bioethanol plants throughout the U.S. Using high-resolution geospatial data from Geographic Information Systems-Multi Criteria Evaluation (GIS-MCE) a biorefinery suitability model has been developed for identifying feasible sites and appropriate biofuel production capacity in the U.S. The biomass feedstocks considered for analysis were switchgrass, miscanthus and corn stover. We conducted a spatial exclusion and preference GIS analysis subjected to environmental and infrastructure criteria combined with biomass yield estimates and identified 164 basic sites and 17 co-location scenarios. Biorefineries using miscanthus feedstock could produce biofuel satisfying a significant portion of the U.S. mandate. This national-scale assessment enhances strategic decision-making capabilities and understanding of spatial distribution of biorefineries.

An integrated multi-feedstock bioenergy (i.e., biofuel, biopower, or bioproduct) supply system has potential to reduce biomass supply system uncertainties and costs. This study identifies optimal configurations of multi-feedstock biomass-to-biorefinery supply chains and pertinent feedstock combinations based on spatial distribution of feedstock and lowest delivered cost to the biorefinery. We used the Supply Characterization Model (SCM) to allocate feedstock supplies to candidate biorefinery facilities. Model runs were performed for herbaceous energy crops, agriculture residue, and woody biomass available in 2017, 2022, 2025, and 2030 as estimated by the Policy Analysis System (POLYSYS) and Forest Sustainable and Economic Analysis Model (ForSEAM) models. Three feedstock supply scenarios were compared: (a) an herbaceous scenario: switchgrass, miscanthus, biosorghum, and corn stover; (b) a woody scenario: coppice wood, noncoppice wood, whole trees, and forestry residues, and (c) a mixed scenario: a combination of all feedstocks in herbaceous and woody scenarios. By 2030 the analyses predicted that 323, 168, and 473 biorefineries were sited in the herbaceous, woody, and mixed scenario, respectively, in the conterminous USA. Feedstock mixes supplied to the biorefineries were mostly dominated by a single feedstock. The most prominent feedstock mixes identified were: † (1) switchgrass and miscanthus; (2) coppice and noncoppice wood; and (3) coppice wood, noncoppice wood, switchgrass and miscanthus. Biorefineries using multi-feedstock would be beneficial for growth of bioeconomy, however flexible and cost-effective conversion platforms should be developed to efficiently utilize multiple feedstocks. This analysis identifies biorefinery locations and feedstock supply mixes while minimizing delivered feedstock costs based on spatial and temporal feedstock availability.