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The University of Texas at Austin’s Policy Research Project (PRP), a nine-month (two semesters) capstone, is a keystone of the core curriculum at the LBJ School of Public Affairs. In PRPs, small groups of students, under the mentorship of a faculty director, take on real-world problems that require special knowledge and skill sets. PRPs expose students to challenges in formulating and executing research, and in communicating academic research and related complex data to broader stakeholder communities and decision makers. The PRP structure is an innovative and effective approach for integrating research within the teaching and training of graduate students who are preparing themselves to address important real-world problems at the intersection of society, economics, technology, and policy.The project summaries below describe seven papers developed during September 2017 – May 2018 as part of a PRP on “Diffusion of Innovations: Interplay of Social, Economic, Technological, and Policy Drivers in the Solar Industry.” Twenty graduate students, drawn from the LBJ School’s Masters in Public Affairs and Masters in Global Policy Studies programs and the Jackson School Geoscience’s Energy and Earth Resources program, participated in this PRP. Dr. Varun Rai, Associate Professor and Associate Dean for Research at the LBJ School, directed the PRP, with support from his research team including: Dr. Ariane Beck, Dr. Ashok Sekar, D. Cale Reeves, and Erik Funkhouser. Clients for the project included the U.S. Department of Energy (Casey Canfield), Lawrence Berkeley National Laboratory (Ben Hoen, Galen Barbose Joachim Seel, Naïm Darghouth, Ryan Wiser), and National Renewable Energy Laboratory (Benjamin Sigrin, Eric O’Shaughnessy).The seven projects separately addressed one of the following topics: (1) low- and middle-income PV adoption, (2) modeling economic and information intervention design, (3) evaluation of DOE’s Solar in Your Community Challenge, (4) property value impacts near large-scale solar facilities, (5) solar market maturity and evolution of business models, (6) social media data for predicting PV adoption, and (7) individual-level variation in adoption of innovations. Many of the papers relied on data collected and curated by Lawrence Berkeley National Laboratory, including data embedded within the annual Tracking the Sun and Utility-Scale Solar reports. Each of the seven teams in the PRP prepared a research paper. The PRP culminated with a full-day conference at UT Austin in May 2018 to present findings from the seven projects in this PRP to a broad audience of about 75 experts from academia, national labs, industry, and government from across the country.

The University of Texas at Austin’s Policy Research Project (PRP), a nine-month (two semesters) capstone, is a keystone of the core curriculum at the LBJ School of Public Affairs. In PRPs, small groups of students, under the mentorship of a faculty director, take on real-world problems that require special knowledge and skill sets. PRPs expose students to challenges in formulating and executing research, and in communicating academic research and related complex data to broader stakeholder communities and decision makers. The PRP structure is an innovative and effective approach for integrating research within the teaching and training of graduate students who are preparing themselves to address important real-world problems at the intersection of society, economics, technology, and policy.The project summaries below describe seven papers developed during September 2017 – May 2018 as part of a PRP on “Diffusion of Innovations: Interplay of Social, Economic, Technological, and Policy Drivers in the Solar Industry.” Twenty graduate students, drawn from the LBJ School’s Masters in Public Affairs and Masters in Global Policy Studies programs and the Jackson School Geoscience’s Energy and Earth Resources program, participated in this PRP. Dr. Varun Rai, Associate Professor and Associate Dean for Research at the LBJ School, directed the PRP, with support from his research team including: Dr. Ariane Beck, Dr. Ashok Sekar, D. Cale Reeves, and Erik Funkhouser. Clients for the project included the U.S. Department of Energy (Casey Canfield), Lawrence Berkeley National Laboratory (Ben Hoen, Galen Barbose Joachim Seel, Naïm Darghouth, Ryan Wiser), and National Renewable Energy Laboratory (Benjamin Sigrin, Eric O’Shaughnessy).The seven projects separately addressed one of the following topics: (1) low- and middle-income PV adoption, (2) modeling economic and information intervention design, (3) evaluation of DOE’s Solar in Your Community Challenge, (4) property value impacts near large-scale solar facilities, (5) solar market maturity and evolution of business models, (6) social media data for predicting PV adoption, and (7) individual-level variation in adoption of innovations. Many of the papers relied on data collected and curated by Lawrence Berkeley National Laboratory, including data embedded within the annual Tracking the Sun and Utility-Scale Solar reports. Each of the seven teams in the PRP prepared a research paper. The PRP culminated with a full-day conference at UT Austin in May 2018 to present findings from the seven projects in this PRP to a broad audience of about 75 experts from academia, national labs, industry, and government from across the country.

Background: The reservoir of latently infected CD4+ T cells ensures HIV persistence during suppressive antiretroviral therapy (ART). The proliferation of reservoir cells carries provirus along as a passenger and produces [...]

A reliable laboratory index of N availability would be useful for making N recommendations, but no single approach has received broad acceptance across a wide range of soils. We compared several indices over a range of soil conditions to test the possibility of combining indices for predicting potentially mineralizable N ([N.sub.0]). Soils (0-5 and 5-15 cm) from nine tillage studies across the southern USA were used in the evaluations. Long-term incubation data were fit to a first-order exponential equation to determine [N.sub.0], k (mineralization rate), and [N.sub.0.sup.*] ([N.sub.0] estimated with a fixed k equal to 0.054 [wk.sup.-1]). Out of 13 indices, five [total C (TC), total N (TN), N mineralized by hot KC1 (Hot_N), anaerobic N (Ana_N), and N mineralized in 24 d (Nmin_24)] were strongly correlated to NO (r > 0.85) and had linear regressions with [r.sup.2] > 0.60. None of the indices were good predictors of k. Correlations between indices and [N.sub.0.sup.*] improved compared with [N.sub.0], ranging from r = 0.90 to 0.95. Total N and flush of C[O.sub.2] determined after 3 d (Fl_C[O.sub.2]) produced the best multiple regression for predicting [N.sub.0] ([R.sup.2] = 0.85) while the best combination for predicting [N.sub.0.sup.*] ([R.sup.2] = 0.94) included TN, Fl_CO2 Cold_N, and NaOH_N. Combining indices appears promising for predicting potentially mineralizable N, and because TN and Fl_CO2 are rapid and simple, this approach could be easily adopted by soil testing laboratories. Abbreviations: Ana_N, anaerobic N mineralization; TC, total carbon; Ca_hypcl, calcium hypochlorite; Cold_N, KCl extractable N[O.sub.3]--N; CT, conventional tillage; Fl_CO2, flush of C[O.sub.2] during 3 d; Hot_N, hot KCl extractable N[H.sub.4]- -N; Hyd_N, hydrolyzable N; k, mineralization rate constant; TN, total nitrogen; NaOH_N, sodium hydroxide distillable N; [N.sub.0], potentially mineralizable N; [N.sub.0.sup.*], value of [N.sub.0] determined using a fixed value for k, Nmin_24, N mineralization during 24 d; NP, not plowed (prairie soil); NT, no-Tillage; NT+SS, no-tillage with non-inversion subsurface deep tillage; PB_N, phosphate- berate distillable N; POMC, particulate organic matter C; POMN, particulate organic matter N; SM, stubble mulch tillage (sweeps to undercut weeds); ST, strip tillage (in-row subsoil for disruption of subsurface pan and coulters for preparation of narrow strip of tilled soil).

Quantification of the impact of long-term agricultural land use on soil organic C (SOC) is important to farmers and policymakers, but few studies have characterized land use and management effects on SOC across physiographic regions. We measured the distribution and total stock of SOC to a depth of 20 cm under conventional tillage (CvT), conservation tillage (CsT), and pasture in 87 production fields from the Southern Piedmont and Coastal Plain Major Land Resource Areas. Across locations, SOC at a depth of 0 to 20 cm was: pasture (38.9 Mg [ha.sup.-1]) > CsT (27.9 Mg [ha.sup.-1]) > CvT (22.2 Mg [ha.sup.-1]) (P [less than or equal to] 0.02). Variation in SOC was explained by management (41.6%), surface horizon clay content (5.2%), and mean annual temperature (1.0%). Higher day content and cooler temperature contributed to higher SOC. Management affected SOC primarily at the soil surface (0-5 cm). All SOC fractions (i.e., total SOC, particulate organic C, soil microbial biomass C, and potential C mineralization) were strongly correlated across a diversity of soils and management systems (r = 0.85-0.96). The stratification ratio (concentration at the soil surface/concentration at a lower depth) of SOC fractions differed among management systems (P < 0.0001), and was 4.2 to 6.1 under pastures, 2.6 to 4.7 under CsT, and 1.4 to 2.4 under CvT; these results agree with a threshold value of 2 to distinguish historically degraded soils with improved soil conditions from degraded soils. This on-farm survey of SOC complements experimental data and shows that pastures and conservation tillage will lead to significant SOC sequestration throughout the region, resulting in improved soil quality and potential to mitigate C[O.sub.2] emissions. Abbreviations: CMIN, potential C mineralization; CsT, conservation tillage row cropping; CvT, conventional tillage row cropping; MLRA, Major Land Resource Area; MWD, mean-weight diameter; POC, particulate organic carbon; SMBC, soil microbial biomass carbon; SOC, soil organic C; SOM, soil organic matter.