Evaluating Yield Models for Crop Insurance Rating

Generated crop insurance rates depend critically on the distributional assumptions of the underlying crop yield loss model. Using farm level corn yield data from 1972-2008, we revisit the problem of examining in-sample goodness-of-fit measures across a set of flexible parametric, semi-parametric, and non-parametric distributions. Simulations are also conducted to investigate the out-of-sample efficiency properties of several competing distributions. The results indicate that more parameterized distributional forms fit the data better in-sample due to the fact that they have more parameters, but are generally less efficient out-of-sample–and in some cases more biased–than more parsimonious forms which also fit the data adequately, such as the Weibull. The results highlight the relative advantages of alternative distributions in terms of the bias-efficiency tradeoff in both in- and out-of-sample frameworks.Yield distributions, Crop Insurance, Weibull Distribution, Beta Distribution, Mixture Distribution, Out-of-Sample Efficiency, Goodness-of-Fit, Insurance Rating Efficiency, Farm Management, Financial Economics, Land Economics/Use,

Symbiotic Human Gut Bacteria with Variable Metabolic Priorities for Host Mucosal Glycans.

UnlabelledMany symbiotic gut bacteria possess the ability to degrade multiple polysaccharides, thereby providing nutritional advantages to their hosts. Like microorganisms adapted to other complex nutrient environments, gut symbionts give different metabolic priorities to substrates present in mixtures. We investigated the responses of Bacteroides thetaiotaomicron, a common human intestinal bacterium that metabolizes more than a dozen different polysaccharides, including the O-linked glycans that are abundant in secreted mucin. Experiments in which mucin glycans were presented simultaneously with other carbohydrates show that degradation of these host carbohydrates is consistently repressed in the presence of alternative substrates, even by B. thetaiotaomicron previously acclimated to growth in pure mucin glycans. Experiments with media containing systematically varied carbohydrate cues and genetic mutants reveal that transcriptional repression of genes involved in mucin glycan metabolism is imposed by simple sugars and, in one example that was tested, is mediated through a small intergenic region in a transcript-autonomous fashion. Repression of mucin glycan-responsive gene clusters in two other human gut bacteria, Bacteroides massiliensis and Bacteroides fragilis, exhibited variable and sometimes reciprocal responses compared to those of B. thetaiotaomicron, revealing that these symbionts vary in their preference for mucin glycans and that these differences occur at the level of controlling individual gene clusters. Our results reveal that sensing and metabolic triaging of glycans are complex processes that vary among species, underscoring the idea that these phenomena are likely to be hidden drivers of microbiota community dynamics and may dictate which microorganisms preferentially commit to various niches in a constantly changing nutritional environment.ImportanceHuman intestinal microorganisms impact many aspects of health and disease, including digestion and the propensity to develop disorders such as inflammation and colon cancer. Complex carbohydrates are a major component of the intestinal habitat, and numerous species have evolved and refined strategies to compete for these coveted nutrients. Our findings reveal that individual bacteria exhibit different preferences for carbohydrates emanating from host diet and mucosal secretions and that some of these prioritization strategies are opposite to one another. Thus, we reveal new aspects of how individual bacteria, some with otherwise similar metabolic potential, partition to "preferred niches" in the complex gut ecosystem, which has important and immediate implications for understanding and predicting the behavioral dynamics of this community

The location of U.S. sugarbeet production

Cover title.Includes bibliographical references (pages 193-207)

Enhancing Water Quality Data Service Discovery And Access Using Standard Vocabularies

There is a growing need for consistency across the publishing, discovering, integrating and access to scientific datasets, such as water quality data. Such datasets may have varying formats and service interfaces. The Network Common Data Form (NetCDF) is both a software package and a data format for producing array-oriented scientific data, which is commonly used to exchange data, including water quality data. NetCDF datasets are also published through service interfaces using the THREDDS data server. Alternatively water quality datasets can be encoded with standard XML formats such as WaterML 2.0, which can be published with services such as the Open Geospatial Consortium (OGC) community\u27s Web Feature Service interface standard (WFS). However, appropriate interpretation of the content, discovery and interoperability of data depends on common models, schemas and vocabularies, though these may not always be available. Using the water quality vocabulary we have developed, formalized using the Resource Description Framework (RDF) language, and published as Linked Data, we demonstrate the use of such standard vocabularies in existing data services for providing service capability metadata. We also present methods for augmenting existing metadata fields for water quality data specifically in formats such as NetCDF, WaterML 2.0 using standard vocabularies. We show how using standard vocabularies that are encoded and published using semantic technologies can enhance discovery, integration and access to existing data services delivering water quality datasets

Assessment of left atrial volume before and after pulmonary thromboendarterectomy in chronic thromboembolic pulmonary hypertension.

BackgroundImpaired left ventricular diastolic filling is common in chronic thromboembolic pulmonary hypertension (CTEPH), and recent studies support left ventricular underfilling as a cause. To investigate this further, we assessed left atrial volume index (LAVI) in patients with CTEPH before and after pulmonary thromboendarterectomy (PTE).MethodsForty-eight consecutive CTEPH patients had pre- & post-PTE echocardiograms and right heart catheterizations. Parameters included mean pulmonary artery pressure (mPAP), pulmonary vascular resistance (PVR), cardiac index, LAVI, & mitral E/A ratio. Echocardiograms were performed 6 ± 3 days pre-PTE and 10 ± 4 days post-PTE. Regression analyses compared pre- and post-PTE LAVI with other parameters.ResultsPre-op LAVI (mean 19.0 ± 7 mL/m2) correlated significantly with pre-op PVR (R = -0.45, p = 0.001), mPAP (R = -0.28, p = 0.05) and cardiac index (R = 0.38, p = 0.006). Post-PTE, LAVI increased by 18% to 22.4 ± 7 mL/m2 (p = 0.003). This change correlated with change in PVR (765 to 311 dyne-s/cm5, p = 0.01), cardiac index (2.6 to 3.2 L/min/m2, p = 0.02), and E/A (.95 to 1.44, p = 0.002).ConclusionIn CTEPH, smaller LAVI is associated with lower cardiac output, higher mPAP, and higher PVR. LAVI increases by ~20% after PTE, and this change correlates with changes in PVR and mitral E/A. The rapid increase in LAVI supports the concept that left ventricular diastolic impairment and low E/A pre-PTE are due to left heart underfilling rather than inherent left ventricular diastolic dysfunction

Insuring Uncertainty in Value-Added Agriculture: Ethanol Production

A wide variety of insurance products is available to agricultural producers to insure against yield or price risks in the markets for the raw commodities they produce. Value-added enterprises, such as ethanol production, have been expanding over the last decade. This paper outlines the development of an insurance product aimed at corn producers who are members of an ethanol production cooperative. The product has the potential to provide these producers with a new and useful risk management tool to insure against price risks in the markets for corn, distillers dried grains with solubles (DDGS), ethanol, and natural gas. Monte Carlo analysis is used to develop fair premiums at various coverage levels. A historical correlation structure is imposed on the simulated price data using a method proposed by Iman and Conover (1982), which maintains the marginal distributions of the variables. Historical analysis is carried out to examine how the product would have performed had it been offered over the last decade. The product is shown to perform as intended, paying indemnities in years of extreme price volatility