13 pages, via Online Journal, This paper contributes to our understanding of farm data value chains with assistance from 54 semi-structured interviews and field notes from participant observations. Methodologically, it includes individuals, such as farmers, who hold well-known positionalities within digital agriculture spaces—platforms that include precision farming techniques, farm equipment built on machine learning architecture and algorithms, and robotics—while also including less visible elements and practices. The actors interviewed and materialities and performances observed thus came from spaces and places inhabited by, for example, farmers, crop scientists, statisticians, programmers, and senior leadership in firms located in the U.S. and Canada. The stability of “the” artifacts followed for this project proved challenging, which led to me rethinking how to approach the subject conceptually. The paper is animated by a posthumanist commitment, drawing heavily from assemblage thinking and critical data scholarship coming out of Science and Technology Studies. The argument’s understanding of “chains” therefore lies on an alternative conceptual plane relative to most commodity chain scholarship. To speak of a data value chain is to foreground an orchestrating set of relations among humans, non-humans, products, spaces, places, and practices. The paper’s principle contribution involves interrogating lock-in tendencies at different “points” along the digital farm platform assemblage while pushing for a varied understanding of governance depending on the roles of the actors and actants involved.
Yu Jin (author), Huffman, Wallace E. (author), and Department of Economics, Shanghai University of Finance and Economics
Department of Economics, Iowa State University
Format:
Journal article
Publication Date:
2016
Published:
Wiley Periodicals, Inc.
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 16 Document Number: D10455
17 pages., Via online journal., This article provides new estimates of the marginal product of public agricultural research and extension on state agricultural productivity for the U.S., using updated data and definitions, and forecasts of future agricultural productivity growth by state. The underlying rationale for a number of important decisions that underlie the data used in cost‐return estimates for public agricultural research and extension are presented. The parameters of the state productivity model are estimated from a panel of contiguous U.S. 48 states from 1970 to 2004. Public research and extension are shown to be substitutes rather than complements. The econometric model of state agricultural TFP predicts growth rates of TFP for two‐thirds of states that is less than the past trend rate. The results and data indicate a real social rate of return to public investments in agricultural research of 67% and to agricultural extension of 100+%. The article concludes with guidance for TFP analyses in other countries.
