This book showcases the power of economic principles to explain and predict issues and current events in the food, agricultural, agribusiness, international trade, natural resources and other sectors. The result is an agricultural economics textbook that provides students and instructors with a clear, up-to-date, and straightforward approach to learning how a market-based economy functions, and how to use simple economic principles for improved decision making. While the primary focus of the book is on microeconomic aspects, agricultural economics has expanded over recent decades to include issues of macroeconomics, international trade, agribusiness, environmental economics, natural resources, and international development. Hence, these topics are also provided with significant coverage.

This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards bringing the benefits of advanced analytics and prognostics capabilities to small scale farmers worldwide. This volume provides data science and software engineering teams with the skills and tools to fully utilize economic models to develop the software capabilities necessary for creating lifesaving applications. The book introduces essential agricultural economic concepts from the perspective of full-scale software development with the emphasis on creating niche blue ocean products. Chapters detail several agricultural economic and AI reference architectures with a focus on data integration, algorithm development, regression, prognostics model development and mathematical optimization. Upgrading traditional AI software development paradigms to function in dynamic agricultural and economic markets, this volume will be of great use to researchers and students in agricultural economics, data science, engineering, and machine learning as well as engineers and industry professionals in the public and private sectors.