Food Barons 2022, Crisis Profiteering, Digitalization and Shifting Power, by ETC Group (https://www.etcgroup.org/files/files/food-barons-2022-full_sectors-final_16_sept.pdf )
Census of Ag shows more adoption of autonomous technology
Baur, P., Iles, A. Replacing humans with machines: a historical look at technology politics in California agriculture. Agric Hum Values 40, 113–140 (2023). https://doi.org/10.1007/s10460-022-10341-2
Corporate concentration (Howard, 2023)
Agricultural Carbon Markets, Payments, and Data: Big Ag’s Latest Power Grab
ETC Group. 2022. Food Barons 2022: Crisis Profiteering, Digitalization and Shifting Power. Available at: https://www.etcgroup.org/files/files/food_barons-summary-web.pdf.
Canadian Agri-food Automation and Intelligence Network - smart farms. Federal and Alberta government funding
Pan-Canadian Smart Farm Network
Drone sprayer risk assessment still going on by PMRA but approved in the US.
New training programs being launched, system more oriented to high tech training
Rotz https://www.elgaronline.com/view/edcoll/9781839101731/9781839101731.00017.xml
Tammara Soma, https://www.frontiersin.org/articles/10.3389/fcomm.2021.762201/full
Duncan, E., Rotz, S., Magnan, A., & Bronson, K. (2022). Disciplining Land through Data: The Role of Agricultural Technologies in Farmland Assetization. Sociologia Ruralis.
Rotz, S., E. Duncan, M. Small, J. Botschner, R. Dara, I. Mosby, M. Reed, and E.D.G. Fraser. 2019. The politics of digital agricultural technologies: A preliminary review. Sociologia Ruralis 59 (2): 203–229.
Kelly Bronson. The Immaculate Conception of Data. McGill Queens
Bronson, K., and P. Sengers. 2022. Big Tech meets big Ag: Diversifying epistemologies of data and power. Science as Culture 31 (1): 15–28.
Bronson, K., and I. Knezevic. 2016. Big data in food and agriculture. Big Data & Society 3 (1): 1–5.
Lajoie-O’Malley, A., K. Bronson, S. van der Burg, and L. Klerkx. 2020. The future (s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services 45: 101183.
Carolan, M. 2017. Publicising food: Big data, precision agriculture, and co-experimental techniques of addition. Sociologia Ruralis 57 (2): 135–154.
Carolan, M. 2018. ‘Smart’ farming techniques as political ontology: Access, sovereignty and the performance of neoliberal and not-so-neoliberal worlds. Sociologia Ruralis 58 (4): 745–764. https://doi.org/10.1111/soru.12202.
Carolan, M. 2020a. Automated agrifood futures: Robotics, labor and the distributive politics of digital agriculture. The Journal of Peasant Studies 47 (1): 184–207.
Carolan, M. 2020b. Acting like an algorithm: Digital farming plat- forms and the trajectories they (need not) lock-in. Agriculture and Human Values 37: 107–119.
Cobby, R.W. 2020. Searching for sustainability in the digital agriculture debate: An alternative approach for a systemic transition. Teknokultura Revista De Cultura Digital y Movimientos Sociales 17: 224–238.
Ditzler, L., and C. Driessen. 2022. Automating agroecology: How to design a farming robot without a monocultural mindset? Journal of Agricultural and Environmental Ethics 35: 2. https://doi.org/10.1007/s10806-021-09876-x.
Fairbairn, M., Z. Kish, and J. Guthman. 2022. Pitching agri-food tech: Performativity and non-disruptive disruption in Silicon Valley. Journal of Cultural Economy 15 (5): 652–670.
Fraser, A. 2019. Land grab/data grab: Precision agriculture and its new horizons. The Journal of Peasant Studies 46 (5): 893–912. https://doi.org/10.1080/03066150.2017.1415887.
Levidow, L., K. Birch, and T. Papaioannou. 2012. EU agri-innovation policy: Two contending visions of the bio-economy. Critical Policy Studies 6 (1): 40–65.
Prause, L., S. Hackfort, and M. Lindgren. 2021. Digitalization and the third food regime. Agriculture and Human Values 38: 641–655.
Agroecology Europe. 2021. AEEU Webinar on “Technological innovations for the agroecological transition.” Agroecology Europe, May 27.
https://www.foodsystemsjournal.org/index.php/fsj/article/view/1048/1018
Automating agroecology https://link.springer.com/article/10.1007/s10806-021-09876-x?utm_source=toc&utm_medium=email&utm_campaign=toc_10806_35_1&utm_content=etoc_springer_20220202
Big data and information asymmetry and how that contributes to system risks. Public sector seriously outpaced on big data by private firms. Challenges with block chain (Miller, M.(2021). Big data, information asymmetry, and food supply chain management for resilience.Journal of Agriculture, Food Systems, and Community Development, 11(1), 171–182.
Block chain for sustainability? https://www.foodsystemsjournal.org/index.php/fsj/article/view/824/805
Many threats not being recognized, including cyber security problems, malware, extortion, dataset interference causing shutdowns, over and underapplications (Tzachor, A., Devare, M., King, B. et al. Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat Mach Intell 4, 104–109 )
Already issues with digitally-driven equipment and ability to repair, more dependence on technicians that aren't necessarily readily available. Serious issues with technician shortages for equipment dealers on the Prairies, as much as 1000-1500 positions empty. Has the technology outstripped our capacity to service the gear? Certainly the gear is way ahead of the labour market. Among strategies is of course finding skilled foreigners (Garvey, S. 2024. Where did all the service technicians go? WP Jan. 11, p. 37) Electronics not always easy to fix or replace parts.
ETC Group, Jim Thomas, NFU presentation
ETC Group. 2016. Software vs. hardware vs. nowhere. QC: ETC Group.
Corporate concentration in big data (see also Carolan, 2020). Bayer controls over half of Digital agricultural data market 60 million hectares, 23 countries, 70 partners. Headed to a new business model. Climate Fieldview. AI in the cloud applied toyour data and then prescriptions from Bayer services and products to solve the problem. They believe there are 40 key decisions and they want to influence all of them. Many other companies, platforms, trying to get as much data as they can. Facial and tracking systems for animals. Data on every cow. Using block chain. AWS (Amazon) is a third of the cloud market and 63% of their profits.
Ag soon to be the second largest user of drones for visuals and applications. Massive uptick in mini-robots or large field scale. Robotics in high value horticulture
Soon to be a $14 billion market? Precision in biotech and precision in big data. LInk genomics data to field data. Also linked to loss of right to repair because they want the data and they want control over the tractor.
Tillable, the Airbnb of land rentals. Using big data to push up the value and price of rented land forTillable tenants. Aggregated data is not individual farmer data anymore.
Prices of big data for farmers is usually very cheap, so where are they selling for profits? INvestors, insurance, traders, etc. Free Field View for buying Bayer products. Lockins, Outcome based pricing. Claim this is sharing the risk, but have to give all the data and buy their products. Farmer doesn't know how price estimates are set. Farmers get balance if their low, excess shared with Bayer
What does it mean for workers? Robots augment more than replace. Changes who can do the work, what kind of skills they need. Warehouse roboticization. Increases speed, monotony, more injuries 50% higher in robot warehouses.
Social and racial biases in the data and systems. Only as equitable as the data provided. Since the data is biased, so are the outputs.
Digital companies claim they're regenerative. Can they use it for sequestration and carbon trading and credits. Big money for Bayer. Carbon credits more valuable than crops? But so much embedded energy in data systems, is that accounted for in climate impacts? About 5 Kwh of electricity to support every GB of data, about 50 cents / GB and that doesn't include storage and processing. AI processing hugely consumptive, like 1 session = 700,000 km trip in a car? A full LCA is hugely polluting and energy consumptive.
All contributes to less resilience?
Digital agricultural technology is dominated by large firms, often the same ones that are part of the agrichemical complex, often working collaboratively with large engineering firms. Many investors are keen. Autonomous farming. Already issues for farmers trying to fix computer driven equipment.
AI showed up in farm vehicles (and robots) in a broad way around 2015. Lots of linkages to GPS which started earlier. Also weed blasting lasers. Also insect monitoring systems based on the sound the insect makes and the wing beat. All this AI based on massive data sets, much of it proprietary. But some of it is historical public data that a private firm has monetized. Also used in public disease forecasting models which is useful if widely available. Some of the satellites are operated by the Canadian Space Agency (Booker, R. 2021. AG game changing and the intelligence is inside. WP Oct. 14, 45
Autonomous trucking. Very approaches in development, and certainly many obstacles in the near term, technical, economic and regulatory, but one model being pursued fits with the UCC and urban freight village model, whereby trucks are driven by humans to a UCC or village and then changed to automonous vehicles for the long distance highway segments, then back to a UCC. Castaldo, J. 2021. Along for the ride. G&M Oct. 16, B1.
Semi-autonomous sprayers, using cameras, sending signals to individual nozzles.
GRAIN Digital Control, this year
Lends itself to more contracted work with robotics.
It has been understood for years that technology introduction is not a neutral process, it creates winners and losers for labour and gender. Technology impacts relate to relations of power, space and context. Friedland, W.H., A.E. Barton, and R.J. Thomas. 1981. Manufacturing
green gold: capital, labor, and technology in the lettuce industry.
New York: Cambridge University Press.
"The classic Agricultural Economic theory of the Treadmill of Technology (Cochrane 1958) examines the impact of technology in agriculture with a focus on larger structural results and increasing farm consolidation. Cochrane (1958) and colleagues
(Levins and Cochrane 1996) argue that ‘‘farmers constantly try to improve their incomes by adopting new technologies. ‘Early adopters’ make profits for a short while because of their lower unit production cost. As more farmers adopt the technology, however, production goes up, prices go down, and profits are no longer possible even with the lower production costs’’ (550). Other farmers are forced to adopt
technology to compete, but the majority of farmers will be ‘‘lost in the price squeeze and leave room for their more successful neighbors to expand’’ (550). The Treadmill of Technology emphasizes the detrimental impact that technology adoption can have on farm income and the ways in which it encourages farm consolidation as farmers use technology to replace labor and increase production (Levins and Cochrane 1996). Technology is often used to displace or replace agricultural labor, both non-family and family labor (Pfeffer 1992), and technology adoption can significantly alter the farm labor market (Bauer 1969) and vice versa (White et al. 2005). Technology can displace labor and increase the burden on remaining laborers as they struggle to
maintain high levels of production (Dexter 1977; Pfeffer 1992). Conversely, perceived labor shortages can often provide motivation to adopt new technology (White et al.
2005). This reciprocal relationship between labor and technology is cyclical: labor shortages and/or desire to displace and control labor lead to the development of new
technologies, those new technologies in turn reshape labor relations and often increase demands on remaining workers (Friedland et al. 1981; Wells 1996)" Schewe and Stuart 2014
Cochrane, W.W. 1958. Farm prices, myth and reality. Minneapolis:
University of Minnesota Press.
Levins, R.A., and W.W. Cochrane. 1996. The treadmill revisited.
Land Economics 72(4): 550—553.
Internet of Things (IoT): from sensors to GNSS (Global Navigation Satellite Systems), cloud computing, weather and climate modeling, high-speed internet, historical yield data, and LiDAR (Light Detection and Ranging) systems
Current state
US, about 5% of dairy farms use robots, expected to grow 20-30 % annually. US ag drone market expected to hit $2 billion by 2026. Drone uses include counting livestock, monitoring for pests and spraying, monitoring water (e.g., checking irrigation canals), and pollination. Big data as lock-in, like the automobile. Big data is in many variety specific applications "homogenized" so it can be used, supports uniformity because the data aggregation process can't deal with extensive diversity. The expense also locks in particular trajectories. It's a continuation of what happened with ag mechanization, eg., corn hybridization a result of mechanical harvesting (Kloppenburg). Knowledge gained is also knowledge lost, especially ecological knowledge, also associated with scale increases [classic knowing more and more and about less and less]. These technologies are value laden. Public apps (Carolan, M. 2020) Acting like an algorithm: digital farming platforms and the trajectories they (need not) lock‑in. Agriculture and Human Values (2020) 37:1041–1053
Packing plants are looking at robotics for slaughter. Tyson looking at it and JBS bought a robotics company that focuses on lamb which has a more uniform carcass and typically sold bone in. Currently labour is cheaper, but firms say they need robotics to deal with labour problems. But robotics currently not as accurate with operations like deboning of beef and pork which mostly sold deboned, resulting in higher waste. Smaller plants are unlikely to be able to afford robotics (Rude, 2020).
AI in grocery to process big data to make strategic decisions, including demand forecasting which of course effects supply chains. Sensors on equipment such as freezers, produce aisles to know when things going bad. Also AI to optimize consumer campaigns. But algorithms don't work well under stresses like COVID, the data isn't reliable any more (Harris, 2022).
