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.
New-Aaron, Moses (author), Semin, Jessica (author), Duysen, Ellen G. (author), Madsen, Murray (author), Musil, Kelsie (author), and Rautiainen, Risto H. (author)
Format:
Journal article abstract
Publication Date:
2019
Published:
USA: Taylor & Francis
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 25 Document Number: D10537
8 pages., via online journal., The Bureau of Labor Statistics (BLS) publishes annual statistics on occupational injuries and fatalities in the United States. The BLS fatality data include all agricultural workers while the non-fatal injury data only cover hired employees on large farms. In 2012, the Central States Center for Agricultural Safety and Health (CS-CASH) began collecting regional media monitoring data of agricultural injury incidents to augment national statistics. The aims of this report were: a) to compare CS-CASH injury and fatality data collected via print and online sources to data reported in previous studies, and b) to compare fatality data from media monitoring to BLS Census of Fatal Occupational Injuries (CFOI) data. CS-CASH media monitoring data were collected from a news clipping service and an internet detection and notification system. These data covered years 2012–2017 in seven Midwestern states (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota). CS-CASH occupational fatality data were compared with aggregate CFOI data for the region during 2012–2015. Media monitoring captured 1048 injury cases; 586 (56%) were non-fatal and 462 (44%) were fatal. The numbers of occupational fatality cases from media monitoring and CFOI were nearly identical (280 vs. 282, respectively), and the distributions by type of injury were similar. Findings suggest that media monitoring can capture equal numbers of fatalities compared to CFOI. Non-fatal injuries, not captured by national surveillance systems, can be collected and tracked using print and electronic media. Risk factors, identified in media sources, such as gender, age, time, and source of the incident are consistent with previously reported data. Media monitoring can provide timely access to detailed information on individual cases, which is important for detecting unique and emerging hazards, designing interventions and for setting policy and guiding national strategies.
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.
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.
2 pages, via online magazine archive, Several years ago, Farm Market iD saw that agribusinesses were struggling to use to the data and insights at their disposal to understand how they were performing in the market and needed modern-day data science to power decision-making. Given Farm Market iD's unique and powerful data and our ability to contextualize data to understand and interpret the agricultural market, we knew we had something valuable to offer.
