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Wallace Huffman continued the tradition of research on Midwest rural labor markets at Iowa State University that was begun in the 1930s by his advisers T.W. Schultz and D. Gale Johnson. We review the lessons learned from this research about the wisdom of policies aimed at retaining population in rural areas in the face of market forces and technological changes that create incentives to migrate to urban areas. Professor Huffman's teaching and lessons learned from the Iowa State Human Resources Workshop continues to shape recent research on the roles of agglomeration economies, information technologies, and returns to human capital on the strength of rural labor markets and policies regarding rural economic development.
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Agriculture ranks one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as scientific foundation for forming effective remediation strategies. However, the methods capable of accurately and efficiently calculating spatially explicit life cycle global warming and eutrophication impacts at a fine spatial scale over a geographic region are lacking. The objective of this study was to compare two regression models for estimating spatially explicit life cycle global warming and eutrophication, with corn production in the Midwest region as a demonstrating example. The results indicated that the gradient boosting regression tree model built with monthly weather features yielded higher predictive accuracy for life cycle global warming impact and life cycle EU. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required longer training time. Additionally, all machine learning models were million times faster than the traditional process-based model and were suitable for use in computationally-intensive applications like optimization and predication. © 2019 IEEE.
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