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.
7 pages, This study aims to identify whether there is dependence between agricultural commodities traded on the Brazilian market. We used the bivariate copula method over a ten-year period to assess the extreme effects on the returns of the following commodities: soybean, wheat, Arabica coffee, and Robusta coffee. The relationship directly affects the dependence between Arabica and Robusta coffees commodities. While the relationship between wheat, Arabica and Robusta coffees, and soybean is positively dependent. Economic growth, market dynamics, and the prices of an agricultural commodity tend to increase the price of other commodities.
