Welfare Impacts of Rural to Urban Water Transfers: An Equilibrium Displacement Approach

Food Security and Poverty,

Continuum of Risk Analysis Methods to Assess Tillage System Sustainability at the Experimental Plot Level

This study applied a broad continuum of risk analysis methods including mean-variance and coefficient of variation (CV) statistical criteria, second-degree stochastic dominance (SSD), stochastic dominance with respect to a function (SDRF), and stochastic efficiency with respect to a function (SERF) for comparing income-risk efficiency sustainability of conventional and reduced tillage systems. Fourteen years (1990–2003) of economic budget data derived from 35 treatments on 36 experimental plots under corn (Zea mays L.) and soybean (Glycine maxL.) at the Iowa State University Northeast Research Station near Nashua, IA, USA were used. In addition to the other analyses, a visually-based Stoplight or “probability of target value” procedure was employed for displaying gross margin and net return probability distribution information. Mean-variance and CV analysis of the economic measures alone provided somewhat contradictive and inconclusive sustainability rankings, i.e., corn/soybean gross margin and net return showed that different tillage system alternatives were the highest ranked depending on the criterion and type of crop. Stochastic dominance analysis results were similar for SSD and SDRF in that both the conventional and reduced tillage system alternatives were highly ranked depending on the type of crop and tillage system. For the SERF analysis, results were dependent on the type of crop and level of risk aversion. The conventional tillage system was preferred for both corn and soybean for the Stoplight analysis. The results of this study are unique in that they highlight the potential of both traditional stochastic dominance and SERF methods for distinguishing economically sustainable choices between different tillage systems across a range of risk aversion. This study also indicates that the SERF risk analysis method appears to be a useful and easily understood tool to assist farm managers, experimental researchers, and potentially policy makers and advisers on problems involving agricultural risk and sustainability

Biosecurity Preparedness Analysis for Poultry Large and Small Farms in the United Arab Emirates

Biosecurity implemented on the poultry farms in the United Arab Emirates (UAE), in the form of preparedness against any possible outbreak of disease, is critical for farm survival, safety, and development. Little information on the status of biosecurity readiness for containing any outbreak of poultry disease is available. This study was conducted to evaluate the status of biosecurity on commercial poultry farms in the UAE. Four categories of biosecurity measures/actions: isolation, human and traffic flow, cleaning, and disinfection, and adoption of vaccination protocols were considered. All 37 licensed commercial poultry farms in the country were enrolled in the study’s survey. Cumulative Distribution Functions (CDFs) and Artificial Neural Network statistical (ANN) methods were used for ranking biosecurity on farms, including a breakdown for large and small farms, and to identify areas that require improvements. The ANN is used to correlate preparedness in the focus areas to the poultry farms’ biophysical and business characteristics, such as the number of yearly flock cycles, farm capacity, the total area of the farms, density, and the number of biosecurity workers. This study finds that more stringent implementation of vaccination protocol, isolation, and human and vehicle-flow controls for disinfection are most needed. The study also revealed that poultry farms address biosecurity preparedness differently based on the type of production on large or small farms, and for broilers or layers

Use of Social Media to Enhance Consumers’ Options for Food Quality in the United Arab Emirates (UAE)

The objective of this research was to study the behavior and attitudes of consumers from the United Arab Emirates towards using the World Wide Web (WWW) for ordering food online, as well as their perception of social media’s (e.g., Facebook, Twitter, Instagram, and WhatsApp) impact on increasing their knowledge about their food quality options. This research question targets social media’s role in aiding consumer decision-making with regard to enhanced food quality choices and thus enhanced food security. The results of this study showed that about 50% of the respondents frequently use a website to order food online in the study area. The analysis of the survey results showed a strong correlation between the frequency of food ordered online by consumers and the number of consumers who sought specific information about food quality, such as those who wished to obtain information about special diets for both medical and non-medical purposes. A strong correlation was also found to exist between the frequency of ordering food online and consumers who often inquired about buying organic food. Furthermore, the authors found the potential and the need for more transparency and enhancement when exchanging information between online food providers and consumers, in order to achieve the country’s food security goal of better consumer access to food quality information

Small Ruminant Production System Efficiency under Abu-Dhabi, United Arab Emirates Arid Land Conditions

Sheep and goat production systems in the United Arab Emirates (UAE) operate under scarce natural resource constraints. A cross-sectional survey that covered 661 mixed farms, including major sheep and goat production, was conducted in the three regions of Abu Dhabi Emirate (Al-Ain, Western Region and Abu Dhabi city) during 2012. A Cobb-Douglas, double-logarithmic stochastic frontier production function and maximum likelihood estimation were applied to estimate important economic derivatives and the associated risk of small ruminant production in this arid area. The highest impact of an input on the output level was found to be labor for raising sheep and alfalfa grass for raising goats. Both labor and alfalfa variables were found to be overutilized for sheep and goat production, respectively. Overall, the results indicate that average technical efficiency is 0.62 for raising sheep and only 0.34 for raising goats in the study area. Technical efficiency analysis included measuring the frequency of farms at each level of estimated technical efficiency in the range between zero and one. Zero for the technical efficiency coefficient indicates a lack of technical efficiency in resource use. The results of this study indicated that only 1% of the sheep farms show a technical efficiency coefficient of 0.25 or less; the same can be said for 41% of goat producers. However, these technical efficiencies were found to be more than 0.75 for 12% and 5% of the sheep and goat farms, respectively. Overall, goat farming in the UAE was found to be less efficient than sheep production. The results also indicated that flock size and type of breed were the most influential factors relative to other factors, and both show a positive relationship with technical efficiency. Other than flock size, factors, such as owners’ years of experience and management practices, were found to be more influential on goat farming system efficiency relative to sheep farming

Agriculture economic impact of energy alternatives and climate change in Colorado: evidence from an equilibrium displacement approach

Includes bibliographical references (page 7).Colorado agriculture has blossomed with the development of water resources that are used for growing crops, which, in turn, spurs value-added production in the meat, sugar and dairy sectors. Increasing urban development is expected to spur the reallocation of an additional six hundred thousand to one million acre feet of agricultural water to new municipal, industrial and energy demands by 2040. Reallocation and climate change will likely to lead to large scale fallowing of agricultural lands. The purpose of the modeling effort summarized in this document is to better understand the impacts borne by the agricultural economy that result from large scale fallowing

Continuum of Risk Analysis Methods to Assess Tillage System Sustainability at the Experimental Plot Level

This study applied a broad continuum of risk analysis methods including mean-variance and coefficient of variation (CV) statistical criteria, second-degree stochastic dominance (SSD), stochastic dominance with respect to a function (SDRF), and stochastic efficiency with respect to a function (SERF) for comparing income-risk efficiency sustainability of conventional and reduced tillage systems. Fourteen years (1990–2003) of economic budget data derived from 35 treatments on 36 experimental plots under corn (Zea mays L.) and soybean (Glycine maxL.) at the Iowa State University Northeast Research Station near Nashua, IA, USA were used. In addition to the other analyses, a visually-based Stoplight or “probability of target value” procedure was employed for displaying gross margin and net return probability distribution information. Mean-variance and CV analysis of the economic measures alone provided somewhat contradictive and inconclusive sustainability rankings, i.e., corn/soybean gross margin and net return showed that different tillage system alternatives were the highest ranked depending on the criterion and type of crop. Stochastic dominance analysis results were similar for SSD and SDRF in that both the conventional and reduced tillage system alternatives were highly ranked depending on the type of crop and tillage system. For the SERF analysis, results were dependent on the type of crop and level of risk aversion. The conventional tillage system was preferred for both corn and soybean for the Stoplight analysis. The results of this study are unique in that they highlight the potential of both traditional stochastic dominance and SERF methods for distinguishing economically sustainable choices between different tillage systems across a range of risk aversion. This study also indicates that the SERF risk analysis method appears to be a useful and easily understood tool to assist farm managers, experimental researchers, and potentially policy makers and advisers on problems involving agricultural risk and sustainability.This article is from Sustainability 3 (2011): 1035–1063, doi:10.3390/su3071035.</p

The Significance of Consumer’s Awareness about Organic Food Products in the United Arab Emirates

Awareness about negative externalities generated by conventional farming is gaining momentum with consumers around the world, opting for alternatively, namely organically, produced food products. Information about consumers’ awareness is an essential element for farmers and marketing agencies to successfully plan production that can capture a greater market share. This study discusses effective factors influencing consumers’ awareness about the benefits of organic food in the United Arab Emirates. Sample data and ordinary least square (OLS) regression techniques are applied to delineate factors influencing consumers’ awareness about organic food. The results from this regression analysis highlight the importance of specific socioeconomic determinants that change awareness about organic food products in United Arab Emirates (UAE) households. This study finds that awareness about organic food is influenced more effective factors such as gender, nationality, and education as well as income, occupation and age. These research findings apply to other economies and societies that have an increasing per capita spending on organic food, but also where people are highly sensitive to information provided about organic food. Therefore, these results are important to these research beneficiaries including food marketing planners, researchers, and agricultural and food policy makers