Climate Effects on Rainfall Index Insurance Purchase Decisions

Rainfall Index insurance is a pilot insurance product offered to producers of hay and pasture in 9 states. This analysis examines the expected payoff of the RI insurance for bi-monthly periods based on rainfall shortage probabilities in alternative climate phases. Differences in expected returns indicate that selection of ENSO-specific optimal intervals may result in higher returns than those based on pooled rainfall series.rainfall insurance, ENSO, expected indemnity, Production Economics, Risk and Uncertainty,

Determinants of Agricultural Disaster Payments in the Southeastern U.S.: County Level Analysis

Using county level data we study if weather and climate variables or variables used as proxies for rent-seeking behavior determine disaster payment in the Southeast. We do not find evidence of rent-seeking but find that, in addition to weather, long term climate variables affect disaster payments.Farm Management, Risk and Uncertainty,

Climate Effects on Rainfall Index Insurance Purchase Decisions

Rainfall Index (RI) insurance provides forage and hay producers with group risk protection against drought related losses. However, insurance premiums and risk protection are currently based on pooled weather data series and do not account for the impacts of specific climate phases, specifically the El Niño Southern Oscillation (ENSO), on local rainfall distribution. This analysis examines differences in the expected payoffs on the RI insurance under varying coverage levels based on probabilities of rainfall shortage during specific climate events at four Agricultural Experiment Stations in Alabama. Policy makers and producers are expected to benefit from the results that show the varying effects of climate on expected payouts from this insurance.Risk and Uncertainty,

Analysis of Irrigated Corn Production Adoption Decisions in Alabama

Expanding ethanol markets, abundance of water resources, and predominance of rainfed corn production in Alabama suggest possibility of irrigating cornfields. Numerical analysis shows that irrigated production becomes more preferable with higher corn prices and risk aversion. Adoption threshold is estimated at the price below its current level.Agribusiness, Crop Production/Industries, Land Economics/Use,

Identifying ENSO Phase Impacts on Area Yield Insurance Rates: An Application of Non-Parametric Analysis

The paper reports results of non-parametric analysis of peanut, corn, and cotton yield distributions by the ElNino Southern Oscillation (ENSO) phases in the Southeastern U.S. For validation purposes, the historical yield data is complemented by a set of simulated peanut yields generated using daily weather data. The hypothesis, justified by the observed South-Eastern climate differences and research on ENSO cycles and planting dates, is that different climate conditions during ENSO cycles translate into different yield distributions and, therefore, insurance premiums (loss to coverage ratios). Kernel density estimates of historical county yield data show consistent patterns in the actuarially fair rate schedules grouped by ENSO phases and geographical areas. In particular, corn and cotton yield insurance premiums appear to be the most dependent on the ENSO phases and are the highest, regardless of coverage, during ElNino and the lowest during LaNina. Peanut premiums are higher during Neutral years and lowest during LaNina. The results appear to be robust to the transformations used to make the yield series stationary. While these dependencies do not necessarily correspond to the precipitation and solar radiation characteristics of the corresponding ENSO cycles in the Southeastern US, drawing direct analogies with yield variability is premature as many less documented factors, like the spacing of sunny and rainy days, may be just as important. The comparisons of the empirical and simulated peanut yield distributions show that they are similar in many ways and that the dissimilarities can be explained by known factors. These findings should be more relevant for the area yield insurance as opposed to the APH arrangements as the yield data used in designing contracts for the former reflects the systemic risk more influenced by climate than by the farm-level, basis risk factors accommodated in the APH plans.Risk and Uncertainty, Q140, C220, G220,

Non-Parametric Analysis of ENSO Impacts on Yield Distributions: Implications for GRP Contract Design

The paper reports preliminary results of non-parametric analysis of historical and crop model generated peanut yield series in the Southwest Georgia. The results suggest ENSO phase dependent differences in yield distributions that are similar for both the simulated and actual series. The differences are magnified in GRP insurance premiums.Crop Production/Industries,

Dynamical modeling of syncytial mitotic cycles in Drosophila embryos

Immediately following fertilization, the fruit fly embryo undergoes 13 rapid, synchronous, syncytial nuclear division cycles driven by maternal genes and proteins. During these mitotic cycles, there are barely detectable oscillations in the total level of B-type cyclins. In this paper, we propose a dynamical model for the molecular events underlying these early nuclear division cycles in Drosophila. The model distinguishes nuclear and cytoplasmic compartments of the embryo and permits exploration of a variety of rules for protein transport between the compartments. Numerical simulations reproduce the main features of wild-type mitotic cycles: patterns of protein accumulation and degradation, lengthening of later cycles, and arrest in interphase 14. The model is consistent with mutations that introduce subtle changes in the number of mitotic cycles before interphase arrest. Bifurcation analysis of the differential equations reveals the dependence of mitotic oscillations on cycle number, and how this dependence is altered by mutations. The model can be used to predict the phenotypes of novel mutations and effective ranges of the unmeasured rate constants and transport coefficients in the proposed mechanism

Modern technologies and artificial intelligence in archaeology and bioarchaeology

This paper discusses the importance of adopting and applying new technologies in scientific fields to increase the rate of progress. It emphasises the need for networking and multidisciplinary collaboration to apply technologies developed for other purposes to solve scientific or professional issues. The paper reviews modern technologies used in archaeology and bioarchaeology, including ground penetrating radar, LiDAR, drones, 3D printing, remote sensing, GIS, and portable X-ray fluorescence. It also presents modern technologies in bioarchaeology such as DNA analysis, stable isotope analysis, radiocarbon dating, microscopic analysis, CT and MRI, and proteomics. The paper introduces palaeoradiology, a branch of radiology that uses imaging technologies to examine bioarchaeological or even archaeological material, and discusses its importance in gaining knowledge about the health, lifestyle, and causes of death of past populations

Cyclic Plasticity and Low Cycle Fatigue of an AISI 316L Stainless Steel: Experimental Evaluation of Material Parameters for Durability Design

AISI 316L stainless steels are widely employed in applications where durability is crucial. For this reason, an accurate prediction of its behaviour is of paramount importance. In this work, the spotlight is on the cyclic response and low-cycle fatigue performance of this material, at room temperature. Particularly, the first aim of this work is to experimentally test this material and use the results as input to calibrate the parameters involved in a kinematic and isotropic nonlinear plasticity model (Chaboche and Voce). This procedure is conducted through a newly developed calibration procedure to minimise the parameter estimates errors. Experimental data are eventually used also to estimate the strain–life curve, namely the Manson–Coffin curve representing the 50% failure probability and, afterwards, the design strain–life curves (at 5% failure probability) obtained by four statistical methods (i.e., deterministic, “Equivalent Prediction Interval”, univariate tolerance interval, Owen’s tolerance interval for regression). Besides the characterisation of the AISI 316L stainless steel, the statistical methodology presented in this work appears to be an efficient tool for engineers dealing with durability problems as it allows one to select fatigue strength curves at various failure probabilities depending on the sought safety level