Modeling Endogenous Technological Change for Climate Policy Analysis

The approach used to model technological change in a climate policy model is a critical determinant of its results. We provide an overview of the different approaches used in the literature, with an emphasis on recent developments regarding endogenous technological change, research and development, and learning. Detailed examination sheds light on the salient features of each approach, including strengths, limitations, and policy implications. Key issues include proper accounting for the opportunity costs of climate-related knowledge generation, treatment of knowledge spillovers and appropriability, and the empirical basis for parameterizing technological relationships. No single approach appears to dominate on all these dimensions, and different approaches may be preferred depending on the purpose of the analysis, be it positive or normative.exogenous, technology, R&D, learning, induced

An Analysis of Factors Affecting the Oxygen Consumption of the Isopod Ligia oceanica

This is the publisher's version, also available electronically from http://www.jstor.org/stable/info/30155682A multiple-regression equation was derived in which the statistical significance of the effects of six independent variables on metabolic rate could be arranged in the following order: exposure temperature, body weight, starvation period, feeding period, acclimation temperature, and percentage of lipids. Two multiple-regression equations were required to express the metabolism of Ligia oceanica without loss of accuracy when reapplied to the individual experimental data. One equation accounted for 84% of the variation of metabolism in animals acclimated to 5 or 12 C; the second accounted for 85% of the variation of metabolism in animals acclimated to 18 or 26 C. The rate:temperature curve for aerobic metabolism was sigmoid. A region of reduced temperature sensitivity occurred at intermediate exposure temperatures. Thermal acclimation had little effect on the level of metabolism of well-fed Ligia. The maximum metabolic rate shifted from 27.6 C in fed animals acclimated at 5 C to 28.2 C in those acclimated at 12 C, to 33.1 C in those acclimated at 18 C, and to 33.4 C in those acclimated at 26 C. The region of reduced temperature sensitivity shifted from 15-25 C at low acclimation temperatures to 20-35 C at high acclimation temperatures. The effects of starvation on oxygen consumption are controlled by body size, duration of starvation, and acclimation temperature. Metabolism is suppressed in small animals sooner than large ones at each acclimation temperature; high acclimation temperatures enhance the onset and magnitude of such effects. Exposure temperature and the period of starvation influenced the effect of body size on metabolism

Estimating corn emergence date using UAV-based imagery

Assessing corn (Zea Mays L.) emergence uniformity soon after planting is important for relating to grain production and for making replanting decisions. Unmanned aerial vehicle (UAV) imagery has been used for determining corn densities at vegetative growth stage 2 (V2) and later, but not as a tool for detecting emergence date. The objective of this study was to estimate days after corn emergence (DAE) using UAV imagery. A field experiment was designed with four planting depths to obtain a range of corn emergence dates. UAV imagery was collected during the first, second and third weeks after emergence. Acquisition height was approximately 5m above ground level resulted in a ground sampling distance 1.5 mm pixel-1. Seedling size and shape features derived from UAV imagery were used for DAE classification based on the Random Forest machine learning model. Results showed image features were distinguishable for different DAE (single day) within the first week after initial corn emergence with a moderate overall classification accuracy of 0.49. However, for the second week and beyond the overall classification accuracy diminished (0.20 to 0.35). When estimating DAE within a three-day window (± 1 DAE), overall 3-day classification accuracies ranged from 0.54 to 0.88. Diameter, area, and major axis length/area were important image features to predict corn DAE. Findings demonstrated that UAV imagery can detect newly-emerged corn plants and estimate their emergence date to assist in establishing emergence uniformity. Additional studies are needed for fine-tuning image collection procedures and image feature identification in order to improve accuracy

Early corn stand count of different cropping systems using UAV-imagery and deep learning

Optimum plant stand density and uniformity is vital in order to maximize corn (Zea mays L.) yield potential. Assessment of stand density can occur shortly after seedlings begin to emerge, allowing for timely replant decisions. The conventional methods for evaluating an early plant stand rely on manual measurement and visual observation, which are time consuming, subjective because of the small sampling areas used, and unable to capture field-scale spatial variability. This study aimed to evaluate the feasibility of an unmanned aerial vehicle (UAV)-based imaging system for estimating early corn stand count in three cropping systems (CS) with different tillage and crop rotation practices. A UAV equipped with an on-board RGB camera was used to collect imagery of corn seedlings (~14 days after planting) of CS, i.e., minimum-till corn-soybean rotation (MTCS), no-till corn-soybean rotation (NTCS), and no-till corn-corn rotation with cover crop implementation (NTCC). An image processing workflow based on a deep learning (DL) model, U-Net, was developed for plant segmentation and stand count estimation. Results showed that the DL model performed best in segmenting seedlings in MTCS, followed by NTCS and NTCC. Similarly, accuracy for stand count estimation was highest in MTCS (R2 = 0.95), followed by NTCS (0.94) and NTCC (0.92). Differences by CS were related to amount and distribution of soil surface residue cover, with increasing residue generally reducing the performance of the proposed method in stand count estimation. Thus, the feasibility of using UAV imagery and DL modeling for estimating early corn stand count is qualified influenced by soil and crop management practices

Environmental Implications of Increased Bioenergy Production on Midwest Soil Landscapes [abstract]

Only abstract of poster available.Track III: Energy InfrastructurePrairie soil landscapes encompass over 16 million acres in Missouri and surrounding states. Much of this area has been degraded by erosion but is still used for grain production. Erosion has caused variable topsoil depth within fields which in turn has resulted in greater within-field variability of crop yield, magnified the drought-prone nature of these soils, and lowered the overall soil productivity and ecosystem function. In recent years, pressure on these sensitive soils has risen due to higher demand for grain production, in part for ethanol and biodiesel. In some areas, highly erodible fields which were historically managed as CRP and pasture are being turned into grain crop acres. Thus as new and fluctuating feed and bioenergy markets develop, land management practices will also shift, resulting in changes in soil and water quality of watersheds. This presentation will explore the likely environmental implications of different types of bioenergy production on the soil resource. Further, the positive benefits of potential changes in land use will be in explored. For example, one alternative for sensitive soils is production of perennial grass as a feedstock for coal co-burning plants and for potential future use in cellulosic ethanol production. Perennial grass yields are likely to be less variable than grain yields, both year-to-year and within fields, primarily because of greater resistance to drought. Grass production systems also provide environmental services not obtained from annual grain crops. We will also discuss our work on developing ways to target the most appropriate places in the landscape for grain or perennial production so as to enhance ecosystem services and improve soil and water quality

A long‑term precision agriculture system sustains grain profitability

After two decades of availability of grain yield-mapping technology, long-term trends in field-scale profitability for precision agriculture (PA) systems and conservation practices can now be assessed. Field-scale profitability of a conventional or ‘business-as-usual’ system with an annual corn (Zea mays L.)-soybean (Glycine max [L.]) rotation and annual tillage was assessed for 11 years on a 36 ha field in central Missouri during 1993 to 2003. Following this, a ‘precision agriculture system’ (PAS) with conservation practices was implemented for the next 11 years to address production, profit and environmental concerns. The PAS was multifaceted and temporally dynamic. It included no-till, cover crops, crop rotation changes, site-specific N and variable-rate or zonal P, K and lime. Following a recent evaluation of differences in yield and yield variability, this research compared profitability of the two systems. Results indicated that PAS sustained profits in the majority (97%) of the field without subsidies for cover crops or payments for enhanced environmental protection. Profit was only lower with PAS in a drainage channel where no-till sometimes hindered soybean stands and wet soils caused wheat (Triticum aestivum L.) disease. Although profit gains were not realized after 11 years of PA and conservation practices, this system sustained profits. These results should help growers gain confidence that PA and conservation practices will be successful

Pre-transplant antibody screening and anti-CD154 costimulation blockade promote long-term xenograft survival in a pig-to-primate kidney transplant model

Xenotransplantation has the potential to alleviate the organ shortage that prevents many patients with end-stage renal disease from enjoying the benefits of kidney transplantation. Despite significant advances in other models, pig-to-primate kidney xenotransplantation has met limited success. Preformed anti-pig antibodies are an important component of the xenogeneic immune response. To address this, we screened a cohort of 34 rhesus macaques for anti-pig antibody levels. We then selected animals with both low and high titers of anti-pig antibodies to proceed with kidney transplant from galactose-α1,3-galactose knockout/CD55 transgenic pig donors. All animals received T-cell depletion followed by maintenance therapy with costimulation blockade (either anti-CD154 mAb or belatacept), mycophenolate mofetil, and steroid. The animal with the high titer of anti-pig antibody rejected the kidney xenograft within the first week. Low-titer animals treated with anti-CD154 antibody, but not belatacept exhibited prolonged kidney xenograft survival (>133 and >126 vs. 14 and 21 days, respectively). Long-term surviving animals treated with the anti-CD154-based regimen continue to have normal kidney function and preserved renal architecture without evidence of rejection on biopsies sampled at day 100. This description of the longest reported survival of pig-to-non-human primate kidney xenotransplantation, now >125 days, provides promise for further study and potential clinical translation

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

Study of the q^2-Dependence of B --> pi ell nu and B --> rho(omega)ell nu Decay and Extraction of |V_ub|

We report on determinations of |Vub| resulting from studies of the branching fraction and q^2 distributions in exclusive semileptonic B decays that proceed via the b->u transition. Our data set consists of the 9.7x10^6 BBbar meson pairs collected at the Y(4S) resonance with the CLEO II detector. We measure B(B0 -> pi- l+ nu) = (1.33 +- 0.18 +- 0.11 +- 0.01 +- 0.07)x10^{-4} and B(B0 -> rho- l+ nu) = (2.17 +- 0.34 +0.47/-0.54 +- 0.41 +- 0.01)x10^{-4}, where the errors are statistical, experimental systematic, systematic due to residual form-factor uncertainties in the signal, and systematic due to residual form-factor uncertainties in the cross-feed modes, respectively. We also find B(B+ -> eta l+ nu) = (0.84 +- 0.31 +- 0.16 +- 0.09)x10^{-4}, consistent with what is expected from the B -> pi l nu mode and quark model symmetries. We extract |Vub| using Light-Cone Sum Rules (LCSR) for 0<= q^2<16 GeV^2 and Lattice QCD (LQCD) for 16 GeV^2 <= q^2 < q^2_max. Combining both intervals yields |Vub| = (3.24 +- 0.22 +- 0.13 +0.55/-0.39 +- 0.09)x10^{-3}$ for pi l nu, and |Vub| = (3.00 +- 0.21 +0.29/-0.35 +0.49/-0.38 +-0.28)x10^{-3} for rho l nu, where the errors are statistical, experimental systematic, theoretical, and signal form-factor shape, respectively. Our combined value from both decay modes is |Vub| = (3.17 +- 0.17 +0.16/-0.17 +0.53/-0.39 +-0.03)x10^{-3}.Comment: 45 pages postscript, also available through http://w4.lns.cornell.edu/public/CLNS, submitted to PR