STOCHASTIC EFFICIENCY ANALYSIS USING MULTIPLE UTILITY FUNCTIONS

Evaluating the risk of a particular decision depends on the risk aversion of the decision maker related to the underlying utility function. The objective of this paper is to use stochastic efficiency with respect to a function (SERF) to compare the ranking of risky alternatives using alternative utility functional forms.Research Methods/ Statistical Methods,

Stochastic efficiency analysis with risk aversion bounds: a simplified approach

A method of stochastic dominance analysis with respect to a function (SDRF) is described and illustrated. The method, called stochastic efficiency with respect to a function (SERF), orders a set of risky alternatives in terms of certainty equivalents for a specified range of attitudes to risk. It can be applied for conforming utility functions with risk attitudes defined by corresponding ranges of absolute, relative or partial risk aversion coefficients. Unlike conventional SDRF, SERF involves comparing each alternative with all the other alternatives simultaneously, not pairwise, and hence can produce a smaller efficient set than that found by simple pairwise SDRF over the same range of risk attitudes. Moreover, the method can be implemented in a simple spreadsheet with no special software needed.Risk and Uncertainty,

Contribution of Non-Timber Forest Products to Rural Household Income in Zambia

Non-timber forest products (NTFPs) play an important role in supporting rural livelihoods and food security in Zambia. NTFP-dependent households are poorer, have younger household heads with lower levels of education, and are located closer to district towns than other rural households are. NTFPs are a particularly important source of income in Luapula, Northwestern and Western provinces. • Income from woodfuel represented the greatest share of income for households that participated in NTFPs, and it was the most commonly reported business activity, with 68% of NTFP households reporting income from charcoal and firewood. NTFPs contribute an average of 32% to total household income among participants, with the poorest being more dependent on these sources. • Given the widespread demand for woodfuel and other forest products, it is likely that rural households will continue to engage in the extraction and trade of NTFPs as a business activity. However, charcoal production, if left unchecked, could compromise the integrity of forests and adversely affect the availability of other NTFPs. In order to reduce households’ reliance on charcoal/firewood as an income source, outreach efforts could promote other NTFPs such as wild honey, ants, and mushrooms as business activities. Mushrooms, ants, and caterpillars may particularly be important activities for female-headed households, as more female-headed households derived income from these sources.NON-TIMBER FOREST, ZAMBIA, Agricultural and Food Policy, Consumer/Household Economics,

Economic Feasibility of Ethanol Production from Sweet Sorghum Juice in Texas

The economic feasibility of producing ethanol from sweet sorghum juice is projected using Monte Carlo simulation models to estimate the price ethanol plants will likely have to pay for sweet sorghum and the uncertain returns for ethanol plants. Ethanol plants in high yielding regions will likely generate returns on assets of 11%-12% and in low yield areas the returns on assets will be less than 10%.Sweet Sorghum, Ethanol, Monte Carlo Simulation, Agribusiness, Agricultural Finance, Crop Production/Industries, Farm Management, Risk and Uncertainty, D20 G10 D81 C15,

Outlook for Texas Representative Cotton Farms

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 are projected for representative Texas cotton farms. The analysis was conducted over the 2001-2008 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming operations in Texas’ major cotton production regions came from two sources: - Producer panel cooperation to develop economic information to describe and simulate representative cotton farms. - Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) August 2004 Baseline. The primary objective of the analysis is to determine cotton farms’ economic viability by region through 2008, assuming provisions of the 2002 Farm Bill. The FLIPSIM policy simulation model incorporates the historical price and production risk faced by cotton farmers. This report presents the results of the August 2004 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing annual cash flow deficits and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2008. This report is organized into six sections. The first section summarizes the process used to develop the representative farms and the key assumptions utilized for the farm level analysis. The second section summarizes the FAPRI August 2004 Baseline and the policy and price assumptions used for the representative farm analyses. The third section presents the results of the simulation analyses for cotton farms. The fourth section summarizes and compares cost of production information for the nine cotton farms. Two appendices constitute the final sections of the report. Appendix A provides tables to summarize the physical and financial characteristics for each of the representative cotton farms. Appendix B provides the names of producers, land grant faculty, and industry leaders who cooperated in the panel interview process to develop the representative farms.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,

Benthic storms, nepheloid layers, and linkage with upper ocean dynamics in the western North Atlantic

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Marine Geology 385 (2017): 304–327, doi:10.1016/j.margeo.2016.12.012.Benthic storms are episodic periods of strong abyssal currents and intense, benthic nepheloid (turbid) layer development. In order to interpret the driving forces that create and sustain these storms, we synthesize measurements of deep ocean currents, nephelometer-based particulate matter (PM) concentrations, and seafloor time-series photographs collected during several science programs that spanned two decades in the western North Atlantic. Benthic storms occurred in areas with high sea-surface eddy kinetic energy, and they most frequently occurred beneath the meandering Gulf Stream or its associated rings, which generate deep cyclones, anticyclones, and/or topographic waves; these create currents with sufficient bed-shear stress to erode and resuspend sediment, thus initiating or enhancing benthic storms. Occasionally, strong currents do not correspond with large increases in PM concentrations, suggesting that easily erodible sediment was previously swept away. Periods of moderate to low currents associated with high PM concentrations are also observed; these are interpreted as advection of PM delivered as storm tails from distal storm events. Outside of areas with high surface and deep eddy kinetic energy, benthic nepheloid layers are weak to non-existent, indicating that benthic storms are necessary to create and maintain strong nepheloid layers. Origins and intensities of benthic storms are best identified using a combination of time-series measurements of bottom currents, PM concentration, and bottom photographs, and these should be coupled with water-column and surface-circulation data to better interpret the specific relations between shallow and deep circulation patterns. Understanding the generation of benthic nepheloid layers is necessary in order to properly interpret PM distribution and its influence on global biogeochemistry.Funding for construction of the Bottom Ocean Monitor was provided by Lamont-Doherty Geological Observatory (now Lamont-Doherty Earth Observatory). BOM and mooring deployments and data analysis were funded by the Office of Naval Research (contracts N00014-75-C-0210 and N00014-80-C-0098 to Biscaye and Gardner at Lamont-Doherty; Contracts N00014-79-C-0071 and N00014-82-C-0019 at Woods Hole Oceanographic Institution and ONR Contracts N00014-75-C-0210 and N00014-80-C-0098 at Lamont-Doherty Geological Observatory to Tucholke), Sandia National Laboratories (contract SL-16-5279 to Gardner), the National Science Foundation (contract OCE 1536565 to Gardner and Richardson), Earl F. Cook Professorship (Gardner), and the Department of Energy (contract DE-FG02-87ER-60555 to Biscaye)

Economic Outlook for Representative Cotton Farms Given the August 2004 FAPRI/AFPC Baseline

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 on representative cotton farms are projected in this report. The analysis was conducted over the 2001-2008 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate cotton operations in the nation’s major production regions came from two sources: - Producer panel cooperation to develop economic information to describe and simulate representative cotton farms. - Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) August 2004 Baseline. The primary objective of the analysis is to determine cotton farms’ economic viability by region through the life of the 2002 Farm Bill. The FLIPSIM policy simulation model incorporates the historical price and production risk faced by cotton farmers. This report presents the results of the August 2004 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing annual cash flow deficits and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2008. This report is organized into five sections. The first section summarizes the process used to develop the representative farms and the key assumptions utilized for the farm level analysis. The second section summarizes the FAPRI August 2004 Baseline and the policy and price assumptions used for the representative farm analyses. The third section presents the results of the simulation analyses for cotton farms. Two appendices constitute the final sections of the report. Appendix A provides tables to summarize the physical and financial characteristics for each of the representative cotton farms. Appendix B provides the names of producers, land grant faculty, and industry leaders who cooperated in the panel interview process to develop the representative farms.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,

Economic Outlook for Representative Cotton Farms Given the August 2003 FAPRI/AFPC Baseline

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 on representative cotton farms are projected in this report. The analysis was conducted over the 2001-2007 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming operations in the nation’s major cotton production regions came from two sources: - Producer panel cooperation to develop economic information to describe and simulate representative cotton farms. - Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) August 2003 Baseline. The primary objective of the analysis is to determine the farms’ economic viability by region through the life of the 2002 Farm Bill, given sector level conditions projected in the August 2003 FAPRI Baseline. The FLIPSIM policy simulation model incorporates the historical risk faced by cotton farmers for prices and production. This report presents the results of the August 2003 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing annual cash flow deficits and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2007. This report is organized into five sections. The first section summarizes the process used to develop the representative farms and the key assumptions utilized for the farm level analysis. The second section summarizes the FAPRI August 2003 Baseline and the policy and price assumptions used for the representative farm analyses. The third section presents the results of the simulation analyses for cotton farms. Two appendices constitute the final section of the report. Appendix A provides tables to summarize the physical and financial characteristics for each of the representative farms. Appendix B provides the names of producers, land grant faculty, and industry leaders who cooperated in the panel interview process to develop the representative cotton farms.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,

A new approach to generating research-quality data through citizen science: The USA National Phenology Monitoring System

Phenology is one of the most sensitive biological responses to climate change, and recent changes in phenology have the potential to shake up ecosystems. In some cases, it appears they already are. Thus, for ecological reasons it is critical that we improve our understanding of species’ phenologies and how these phenologies are responding to recent, rapid climate change. Phenological events like flowering and bird migrations are easy to observe, culturally important, and, at a fundamental level, naturally inspire human curiosity— thus providing an excellent opportunity to engage citizen scientists. The USA National Phenology Network has recently initiated a national effort to encourage people at different levels of expertise—from backyard naturalists to professional scientists—to observe phenological events and contribute to a national database that will be used to greatly improve our understanding of spatio-temporal variation in phenology and associated phenological responses to climate change.

Traditional phenological observation protocols identify specific dates at which individual phenological events are observed. The scientific usefulness of long-term phenological observations could be improved with a more carefully structured protocol. At the USA-NPN we have developed a new approach that directs observers to record each day that they observe an individual plant, and to assess and report the state of specific life stages (or phenophases) as occurring or not occurring on that plant for each observation date. Evaluation is phrased in terms of simple, easy-to-understand, questions (e.g. “Do you see open flowers?”), which makes it very appropriate for a citizen science audience. From this method, a rich dataset of phenological metrics can be extracted, including the duration of a phenophase (e.g. open flowers), the beginning and end points of a phenophase (e.g. traditional phenological events such as first flower and last flower), multiple distinct occurrences of phenophases within a single growing season (e.g multiple flowering events, common in drought-prone regions), as well as quantification of sampling frequency and observational uncertainties. These features greatly enhance the utility of the resulting data for statistical analyses addressing questions such as how phenological events vary in time and space, and in response to global change. This new protocol is an important step forward, and its widespread adoption will increase the scientific value of data collected by citizen scientists.
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Representative Farms Economic Outlook for the August 2006 FAPRI/AFPC Baseline

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 on representative crop and livestock operations are projected in this report. The analysis was conducted over the 2004-2011 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming operations in the nation’s major production regions came from two sources: • Producer panel cooperation to develop economic information to describe and simulate representative crop, livestock, and dairy farms, and • Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) August 2006 Baseline. The FLIPSIM policy simulation model incorporates the historical risk faced by farmers for prices and production. This report presents the results of the August 2006 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing negative ending cash reserves and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2011.Agribusiness, Agricultural and Food Policy, Crop Production/Industries, Livestock Production/Industries,