Crop Insurance For Maryland Field Crops And Livestock

Multi-peril crop insurance is a valuable risk management tool which allows you to insure against losses on your farm due to adverse weather conditions, price fluctuations, and unavoidable pests and diseases. It shifts unavoidable production risks to an insurance company for the payment of a fixed amount of premium per acre. This publication assists readers in understanding the basics of the federal crop insurance program.

Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.

From Cash to Electronic Payments: Microeconomic, Macroeconomic, and Policy Aspects of Retail Payment Systems

This dissertation explores three aspects of the economics and policy issues surrounding retail payments (low-value frequent payments): the microeconomic aspect, by measuring costs associated with retail payment instruments; the macroeconomic aspect, by quantifying the impact of the use of electronic rather than paper-based payment instruments on consumption and GDP; and the policy aspect, by identifying barriers that keep countries stuck with outdated payment systems, and recommending policy interventions to move forward with payments modernization. Payment system modernization has become a prominent part of the financial sector reform agenda in many advanced and developing countries. Greater use of electronic payments rather than cash and other paper-based instruments would have important economic and social benefits, including lower costs and thereby increased economic efficiency and higher incomes, while broadening access to the financial system, notably for people with moderate and low incomes. The dissertation starts with a general introduction on retail payments. Chapter 1 develops a theoretical model for measuring payments costs, and applies the model to Guyana—an emerging market in the midst of the transition from paper to electronic payments. Using primary survey data from Guyanese consumers, the results of the analysis indicate that annual costs related to the use of cash by consumers reach 2.5 percent of the country’s GDP. Switching to electronic payment instruments would provide savings amounting to 1 percent of GDP per year. Chapter 2 broadens the analysis to calculate the macroeconomic impacts of a move to electronic payments. Using a unique panel dataset of 76 countries across the 17-year span from 1998 to 2014 and a pooled OLS country fixed effects model, Chapter 2 finds that on average, use of debit and credit cards contribute USD 16.2 billion to annual global consumption, and USD 160 billion to overall annual global GDP. Chapter 3 provides an in-depth assessment of the Albanian payment cards and remittances market and recommends a set of incentives and regulations (both carrots and sticks) that would allow the country to modernize its payment system. Finally, the conclusion summarizes the lessons of the dissertation’s research and brings forward issues to be explored by future research in the retail payments area.