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The main purpose of this study was to specify and estimate an econometric model of the world fiber market, with emphasis on the cotton production and consumption for the major world fiber market participants. In addition to forecasting, the model is used to estimate the impact of some important market and policy developments. Analysis of these developments provides timely and relevant information for many groups and individuals with interest in the fiber market.
The relative prosperity in U.S. agriculture that attended the passage of the Federal Agriculture Improvement and Reform Act of 1996 was followed by a general decline in U.S. agricultural prices from 1998 to 2000. This trend in declining prices continues through the year 2001, despite the movement toward more liberalized agricultural trade. Trade liberalization has been the result of a variety of factors, including the implementation of the Uruguay Round Agreement, and the establishment of a variety of regional trade agreements, such as the North America Free Trade Agreement. Needless to say, in the face of falling agricultural prices and increasingly liberalized ag ricultural trade, the agricultural policy scene is an extremely complex one, both locally and globally. The chapters in this volume look to understand this complexity by ad dressing the interaction between trade, the economic well-being of the farm sector, and the possibilities for future policy reform. The chapters collected here explore a number of different issues, including the operation of the tar iff-rate quotas established under the Uruguay Round Agreement, the impli cations of sanitary and phytosanitary restrictions on trade, and the growing controversy over genetically modified organisms. In addition, several chap ters analyze the interaction between agricultural trade and environmental concerns.
Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data. The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.