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.
USA: National Farm Medicine Center, Marshfield, Wisconsin.
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 133 Document Number: D11390
Notes:
4 pages., News release via online., Describes largest database of publicly available online information about agriculture-related injuries in the U.S. and safety solutions for farm families.
11 pages, via Online journal, The Soil Vulnerability Index (SVI) was developed by the USDA Natural Resources Conservation Service (NRCS) to identify inherent vulnerability of cropland to runoff and leaching. It is a simple index that relies on the SSURGO database and can be used with basic knowledge of ArcGIS. The goal of this study was to investigate a relationship between constituent (sediment and nutrient) loadings and fraction of the watershed in each SVI class. The SVI maps were developed for each of the seven subwatersheds of the Mark Twain Lake watershed in Missouri, which were similar in soil conditions and climatic variability. The SVI assessment was performed by investigating if the distribution of the SVI for cropland in each subwatershed could help explain measured 2006 to 2010 sediment and nutrient loads better than crop distribution alone. Regression analyses were performed between annual loads of sediment and nutrients exported from the watersheds and a composite number that included either cropland distribution alone, or cropland distribution combined with the SVI. Coefficients of determination and p-values were compared to assess the ability of land use and SVI distributions to explain stream loads. Integrating the SVI in the land cover variable improved the ability to explain constituent loads in the watersheds for sediment, total nutrients, and dissolved nitrogen (N). Regression results with and without the SVI were identical for dissolved phosphorus (P), potentially indicating that SVI was not indicative of dissolved P transport at the current site. Overall, the application of the SVI at watershed scale was not perfect, but acceptable at correctly identifying cropland of greatest vulnerability and linking with transported constituent loads.
Online from publisher., Author describes the race that is on "to assemble and prove a tool that can deliver precision data and logistics throughout beef production spaces," connecting producers with consumers.
20 pages., Via online from the University of Illinois website., Authors' review provided an overview of the data sources, computational methods, and applications of text data in the food industry. Applications of text data analysis were illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food systems.
