The following is the third preview installment of an upcoming Smart Rural CommunitySM paper on ag tech. Previous installments provided an overview of the U.S. farm industry and examples of ag tech for crop and livestock production.
AG TECH IN ACTION
ADOPTION FACTORS
1. Farm Size and Costs
Several factors have been identified when assessing the rate and pace of ag tech adoption. Similar to factors affecting broadband adoption, generally, these can be presumed to include cost, perceived relevance/value, and age of user.1 An analysis of farm demographics reveals positive indicators for increased ag tech adoption. As described in earlier installments, U.S. farms are evolving. Although most farms are small, most production occurs on large farms.2 Inasmuch as the cost of ag tech remains a barrier to adoption,3 large farms may realize more beneficial economies than small farms. Overall, farm size has been identified as a prevailing factor that determines adoption.4
The efficiencies of ag tech, which intends to reduce input costs while increasing productivity and yields, may manifest differently on farms of various sizes. For example, a small, owner-operated dairy may deploy automated feed, milking, and manure management technology that can weigh against the need to hire outside, non-family help. In contrast, a large crop farm may deploy a battery of sensors and automated equipment to drive variable rate technology (VRT) enabled efficiencies that multiply across the many acres farmed. Overall, many factors will contribute to anticipated outcomes, and a simple comparison of adopters to non-adopters is difficult because that analysis must contemplate many variables such as size, crops, and sufficient broadband availability to support precision ag (PA) applications. Nevertheless, if farm size is identified as the prevelant factor that informs adoption, then it can be reasoned that overall adoption should increase as farm sizes increase. Adoption data provided by the USDA Economic Research Service (ERS) bears this point, as illustrated in the graph below.5
Source: USDA ERS, 2016 (Schimmelpfennig, 2010 data)
Notable, as well, is the need for broadband in rural areas to support ag tech is inelastic to farm size. Farms, whether large or small, are located primarily in rural regions, and large farms are not necessarily composed of contiguous acres. Rather, large farms often include operations that manage dispersed acreage across a range of many miles. This, too, increases the imperative for ubiquitous broadband deployment in rural agricultural regions. In sum, trends toward larger farms and data indicating higher PA adoption in larger farms demonstrate the need for rural broadband deployments to support increasing PA needs.
In addition to farm size, capital costs inform adoption. And, as noted above, most U.S. farms are small farms; many are owner-operator enterprises. Capital costs for small and medium farms can be mitigated by introducing ag tech with tailored applications. While rudimentary efficiencies can be gained through basic data collection and analysis; greater efficiencies are realized through more sophisticated and comprehensive data collection and analysis, e.g., more expensive solutions. However, the benefits of PA, in addition to improved efficiencies and yield, can extend to quality-of-life improvements. For example, a farmer in a self-steering tractor can avoid fatigue as the machinery operates autonomously and use that time to monitor commodity prices. These labor efficiencies can benefit many small-scale farm operators who depend on off-farm income and who would benefit from that reclaimed time.6
Adoption trends can also be anticipated to correspond to developments in technology and relative pricing. As noted above, perceived value drives adoption. Value can be defined, generally, as the difference between cost and return. Stated differently, users can be expected to adopt ag tech when positive returns on investment outweigh capital costs, operating expenses, and intangible costs associated with acquisition and deployment. Notably, ag tech prices are declining over time.7 This is consistent with technology prices, generally as (i) processing power grows exponentially in relatively quick cycles,8 (ii) ongoing improvements change what is considered “state of the art,” and (iii) increased demand results in higher production, leading to economies of scale that enable lower costs and pricing. A report explains, “Due to the evolution of technology, the size and shape of sensors is getting smaller and more sophisticated, while in parallel the general cost of the IoT devices is getting lower.”9 Decreasing prices for technology are often demonstrated in both lower constant-dollar and nominal dollar values. These overall decreasing costs would be anticipated to presage increased ag tech adoption over time. In this vein, tech adoption can be perceived as a steady incline punctuated by spikes as adoption rates move upward: this may be visualized as a graph that features time on the X axis and adoption on the Y axis. Innovative developments will be represented by an initial spike on the upward sloping X axis that reflects action by early adopters, leading to a higher mark on the Y axis. That spike may then temper to a more moderate incline as initial excitement wanes but as the now-common technology is adopted by a broader community of users. The adoption line may then spike again when either (a) prices drop or (b) a new innovation that attracts early adopters is released.
2. Age and Educational Attainment
As farms change, farmers are changing, too. The average age of a U.S. farmer in 2017 was 57.5 years, 1.2 years older than the average age of a farmer in 2012.10 Although data showing generally that older users adopt technology at lower rates than younger users,11 broadband adoption among “older users” is increasing over time as (a) perceived relevance among users increases as more aspects of daily life go “online,” and (b) users who were in the 50-60 demographic a decade ago (and who were broadband adopters) now populate the 60-70 age group and remain broadband users.12 Moreover, an analysis of ag tech, specifically, should also contemplate the competing role of education in broadband and tech adoption; in particular, increased broadband adoption trends alongside increased educational attainment.13 In these regards, then, it is instructive to assess trends surrounding young farmers. According to the USDA Farm Census, 321,000 farmers are “young farmers,” i.e., 35 years or younger.14 This cadre of young farmers increasingly obtains more post-secondary education than their predecessors. In fact, it is anticipated that nearly 69% of young farmers in the near term will have a college degree.15 These data correlate with increasing educational attainment in rural areas, generally. The ERS reports in 1970, more than half (56%) of rural adults 25 years and older did not have a high school diploma. That share dropped to 15% in 2015.16 Currently, most rural adults have a high school diploma or equivalent (GED), and nearly 30% have a bachelor’s degree or higher. Together, these numbers bode well for the increasing incorporation of technology in agriculture. Young farmers show greater favorability to PA than older counterparts.17 Today’s farmers will find complete programs built around the evolving needs of the ag tech industry. For example, Wisconsin Indianhead Technical College (WITC) offers a two-year associates degree program leading to a technical diploma for Agricultural Power and Equipment Technician. Similarly, the Pennsylvania Department of Agriculture coordinates farming apprenticeship programs.18 Programs like this are often only the beginning, as continuing education is necessary to keep pace with developing technology. These changes presage the evolution of “farm worker” to “farm technician.” As described in a report published by the U.S. Department of Homeland Security, "Where in the past farmers relied on mechanical skills to keep equipment operating, precision agriculture requires them to learn how to integrate computer systems and evaluate data integrity. . . Failure to adopt new technologies puts farmers into a competitive disadvantage. Being able to evaluate which technologies will return on their investment is a critical skill for today’s farmers."19
Overall, trends reflecting age and educational attainment of farmers and farm size indicate opportunities for positive ag tech adoption growth and an ongoing need for rural broadband deployments.
Next installment: ROI and cybersecurity in ag tech.
1 See, Schadelbauer, Rick, “Conquering the Challenges of Broadband Adoption,” Smart Rural Community, NTCA–The Rural Broadband Association, Arlington, VA (2014) (https://www.ntca.org/sites/default/files/legacy/images/stories/Documents/Advocacy/CCBA_Whitepaper.pdf) (visited Apr. 7, 2021).
2 “Farming and Farm Income,” Economic Research Service, USDA (www.ers.usda.gov/data-products/ag-and-food-statistics-charting-the-essentials/farming-and-farm-income) (visited Apr. 6, 2021) (ERS Farming and Farm Income).
3 It bears mention, however, that these optimistic numbers must be analyzed alongside data indicating the growth of farm sizes and decreases in the number of farms; the proliferation of small farms where adoption may lag behind that of larger farms; and the potential impact of farmers who rely on off-farm income, and whether that might discourage investment in ag tech on small farms if the farm is not a primary source of income. Approximately half of U.S. farms are small farms, and “households operating these farms typically rely on off-farm sources for most of their household income.” (ERS Farming and Farm Income.) At the same time, these trends may be counterbalanced by large farms that are managed by young farmers. In this regard, the core target for ag tech adoption would seem to be large farms that are farmed by young farmers.
4 Schimmelpfennig, David, “Farm Profits and Adoption of Precision Agriculture,” Economic Research Service, USDA, at 26 (Oct. 2016) (https://www.ers.usda.gov/webdocs/publications/80326/err-217.pdf?v=0) (visited Apr. 7, 2021) (Schimmelpfennig).
5 The USDA Agricultural Resource Management Survey reports that precision ag was used on 30% to 50% of corn and soybean acres in 2010-2012. These data, however, do not account for other row crops and specialty crops. Overall, although different elements of precision are often used in tandem, GPS is more often used alone (17.2%) than in combination with other technologies. Guidance systems are used more often when adopted alone (6.1% corn farms). GPS is used with guidance systems on 5.7% of farms; with VRT on 4.3% of farms; and GPS, guidance and VRT are used together on 3.8% of farms. (Schimmelpfennig at 6, 10.) These figures refer to farms, but not farm acres.
6 ERS Farming and Farm Income
7“Threats to Precision Agriculture,” 2018 Public-Private Analytic Exchange Program, U.S. Department of Homeland Security, at 9 (https://www.dhs.gov/sites/default/files/publications/2018%20AEP_Threats_to_Precision_Agriculture.pdf) (visited Apr. 6, 2021).
8Commonly referred to as “Moore’s Law,” this principle predicts that the number of transistors on a circuit will double every two years. First forecast in 1965 by American engineer Gordon Moore, it has generally reflected engineering advancements for the past 50 years. The exponential growth of capability has been accompanied by decreasing prices per unit of capability, enabling more powerful chips to be deployed to “general purpose technology,” i.e., products that can be tailored to serve a variety of uses and industries.
9Konstantinos, Demestichas; Peppes, Nikolaus; Alexakis, Theodoros, “Survey on Security Threats and Agricultural IoT and Smart Farming,” Sensors, MDPI, at 3 (2020) (https://www.mdpi.com/1424-8220/20/22/6458/htm) (visited Mar. 19, 2021) (Konstantinos, et al.).
10 “2017 Census of Agriculture Data Release,” National Agricultural Statistics Service, USDA, at 26 (Apr. 11, 2019) (https://www.nass.usda.gov/Newsroom/Executive_Briefings/2019/04-11-2019.pdf) (visited Apr. 7, 2021).
11 Internet/Broadband Fact Sheet, Pew Research (June 12, 2019) (https://www.pewresearch.org/internet/fact-sheet/internet-broadband/) (visited Mar. 17, 2021) (Pew).
12 See, e.g., Greenwald, P., Stern, ME, Clark, S., Sharma, R., “Older Adults and Technology: In Telehealth, They May Not Be Who You Think They Are,” International Journal of Emergency Medicine (2018) (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5752645/) (visited Sep. 14, 2020).
13 See, Lowenberg-DeBoer, Jess, “Yield of Dreams: How Precision Ag Will Help Feed the Planet,” Trend Magazine, Pew, (Jun. 12, 2017) (https://www.pewtrusts.org/en/trend/archive/summer-2017/yield-of-dreams-how-precision-agriculture-will-help-feed-the-planet) (visited Apr. 2, 2021).
14Census of Agriculture: 2017 Census Volume 1, National Agricultural Statistics Service, USDA, at Table 68, (2017) (https://www.nass.usda.gov/Publications/AgCensus/2017/Full_Report/Volume_1,_Chapter_1_US/st99_1_0068_0068.pdf) (visited Apr. 8, 2021).
15 Wyant, Sara, “Six Trends Shaping the Future of Farming and Ranching,” Agri-Pulse Communications, at n. 5 (Feb 17, 2019) (www.agweek/com/opinion/columns/4571716-six-trends-shaping-future-farming-and-ranching) (visited Apr. 6, 2021) (Wyant).
16 “Rural Education at a Glance, 2017 Edition,” Economic Information Bulletin 171, Economic Research Service, United States Department of Agriculture, at 2 (Apr. 2017) (https://www.ers.usda.gov/webdocs/publications/83078/eib-171.pdf?v=6364.1) (visited Apr. 8, 2021).
17 Saiz-Rubio, Veronica, and Rovira-Mas, Francisco, “From Smart Farming Towards Agriculture 5.0: A Review on Crop Data Management,” Agricultural Robotics Laboratory, Universitat Politecnica de Valencia, Camino de Vera, at 2 (2020) (Saiz-Rubio, Rovira-Mas).
18“Wolf Administration Highlights Ag Apprenticeships Providing Hands-On, Paid Training for Jobs in Demand Among Pennsylvania Employers,” Pennsylvania Department of Agriculture (Nov. 9, 2020) (https://www.media.pa.gov/pages/agriculture_details.aspx?newsid=993) (visited Apr. 9, 2021).
19DHS at 14.
