DEMAND FOR FEED GRAINS AND CONCENTRATES BY LIVESTOCK CATEGORY

Livestock feed demand is a collection of derived feed demands by various livestock categories. A structural understanding of demand for feed grains and total concentrates requires knowledge of separate feed demand relationships for each major livestock category. While a number of aggregate livestock feed demand relationships have been estimated, little is known about the structure of feed demand by livestock type. In this study unique livestock feed demand relationships for feed grains and total concentrates are estimated for each of seven major livestock categories. The estimated relationships show substantial differences in elasticities of concentrate and feed grain feed demand with respect to livestock price across livestock groups. Using feed demand parameters by livestock category enables analysts to evaluate policy effects of changes in feed demand quantities and feed costs within the livestock economy, as well as to provide more reliable estimates of the total change in feed demand.Livestock Production/Industries,

Micro-mechanical analysis of damage growth and fracture in discontinuous fiber reinforced metal matrix composites

The near-crack-tip stresses in any planar coupon of arbitrary geometry subjected to mode 1 loading may be equated to those in an infinite center-cracked panel subjected to the appropriate equivalent remote biaxial stresses (ERBS). Since this process can be done for all such mode 1 coupons, attention may be focused on the behavior of the equivalent infinite cracked panel. To calculate the ERBS, the constant term in the series expansion of the crack-tip stress must be retained. It is proposed that the ERBS may be used quantitatively to explain different fracture phenomena such as crack branching

An Illustration of Students’ Engagement with Mathematical Software using Remote Observation

Students using three types of spreadsheet calculators for understanding expected value were observed remotely. This remote observation involves the use of webcams and application sharing for observing students learning mathematics. The study illustrates how remote observation can be used for collecting mathematical education data and raises questions about the extent to which such a method can be used in future experiments

Mathematical thinking of undergraduate students when using three types of software

The research investigates how conceptual understanding of mathematics is promoted when using three types of software: black-box (no mathematical intermediate steps shown), glass-box (intermediate steps shown) and open-box (interaction at each intermediate step). Thirty-eight students were asked to think-aloud and give detailed explanations whilst answering three types of tasks: mechanical (mostly procedural), interpretive (mostly conceptual) and constructive (mixture of conceptual and procedural). The software types had no impact on how students answered the mechanical tasks; however students using the black-box did better on the constructive tasks because of their increased explorations. Students with low maths confidence resorted to using real-life explanations when answering tasks that were application related

Technology - Empowering the Educational Researcher through Remote Observation

Observing students using computers often occurs through three methods: user-lab, on-site and remote data logging. Whilst each of these have their advantages with the new type of students such as elearners, an alternative method called web-conferencing remote observation is presented for observing students at a distance. This method collects both audio and video data of the observer through webcams and voice/video conversations. Students are able to interact with the software through application sharing facilities. Further, it allows both quantitative and qualitative data to be collected. This proof-of-concept method is presented here where it has been used in two previous studies using Windows Messenger and Netviewer. Although, video quality is not high the quality is sufficient for observational data

Short-Run Economic Impacts of Hurricane Katrina (and Rita)

Sturm; Erdölförderung; Offshore-Industrie; Makroökonomischer Einfluss; USA

FARM COMMODITY PAYMENT LIMITS: WHAT IMPACT WILL THEY HAVE ON THE ECONOMIC VIABILITY OF SOUTHEASTERN AGRICULTURE?

Agricultural and Food Policy,

Mitigating Cotton Revenue Risk Through Irrigation, Insurance, and Hedging

This study focuses on managing cotton production and marketing risks using combinations of irrigation levels, put options (as price insurance), and crop insurance. Stochastic cotton yields and prices are used to simulate a whole-farm financial statement for a 1,000 acre furrow irrigated cotton farm in the Texas Lower Rio Grande Valley under 16 combinations of risk management strategies. Analyses for risk-averse decision makers indicate that multiple irrigations are preferred. The benefits to purchasing put options increase with yields, as they are more beneficial when higher yields are expected from applying more irrigation applications. Crop insurance is strongly preferred at lower irrigation levels.cotton, crop insurance, irrigation, options, puts, risk, simulation, stochastic efficiency with respect to a function, Farm Management, Risk and Uncertainty, D81, Q12, Q15,

ESTIMATING PRICE VARIABILITY IN AGRICULTURE: IMPLICATIONS FOR DECISION MAKERS

Using a stochastic version of the POLYSYS modeling framework, an examination of projected variability in agricultural prices, supply, demand, stocks, and incomes is conducted for corn, wheat, soybeans, and cotton during the 1998-2006 period. Increased planting flexibility introduced in the 1996 farm bill results in projections of significantly higher planted acreage variability compared to recent historical levels. Variability of ending stocks and stock-to-use ratios is projected to be higher for corn and soybeans and lower for wheat and cotton compared to the 1986-96 period. Significantly higher variability is projected for corn prices, with wheat and soybean prices also being more variable. No significant change in cotton price variability is projected.POLYSYS model, Price variability, Stochastic simulation, Crop Production/Industries,