How media impacts digital technology adoption in U.S. and Brazilian agriculture
URBANA, Ill. -- Digital technologies on the farm improve efficiency, productivity, and profits, but few farmers are taking full advantage of available tools. According to University of Illinois Urbana-Champaign researchers, communication channels play an important role in farmers’ decision-making process around technology adoption. A new study in the journal Agriculture looks at how traditional media, social media, and interpersonal meetings influence soybean farmers in the U.S. and Brazil, both world leaders in soybean production.
“Like everyone else, farmers are inundated with a constant flow of information, and new technologies appear all the time. However, the role of communication, as it impacts broader adoption decisions, is somewhat understudied,” said lead study author Joana Colussi, an instructor and postdoctoral research associate in the Department of Agricultural and Consumer Economics, part of the College of Agricultural, Consumer and Environmental Sciences (ACES) at Illinois.
Colussi and her coauthors, faculty in ACES and at the Federal University of Rio Grande do Sul in Brazil, surveyed 801 soybean farmers in the biggest soybean growing regions of Brazil and the U.S. to learn what digital technologies they’re using, how beneficial they are, and what communication channels they rely on in deciding to adopt those tools.
As documented in previous studies, survey respondents in the U.S., on average, used more digital technologies — including autosteer, yield monitors, sprayer control systems, and more — than farmers in Brazil. Colussi says that pattern likely reflects the longer availability of precision technologies in the U.S., where most of them were developed.
In both locations, survey respondents felt digital technologies were influential in decision-making and beneficial for farming outcomes, especially regarding the potential for increased efficiency and profitability.
The researchers asked survey respondents to rate the influence of various communication channels on their adoption of digital technologies. In general, both Brazilian and U.S. farmers rated interpersonal meetings, such as field days, conferences, and conversations with Extension agents, retailers, and neighbors above mass media and social media. Brazilian respondents rated social media higher than U.S. farmers and higher than mass media channels.
“Even though social media is increasing in relevance, our results suggest interpersonal meetings are still very important,” Colussi said.
After examining the respondents’ self-reported patterns, the researchers performed a correlation analysis to reveal how much influence each communication channel had on actual adoption patterns in the survey sample.
“The self-reported results show the relevance producers attributed to each communication channel. On the other hand, correlations show the level of association between the technologies adopted and different communication channels analyzed. However, it is important to point out that these relationships between different variables do not imply causality,” Colussi said.
Correlations between communication channels and the decision to adopt various technologies differed in the two countries. For example, the use of yield monitors in Brazil correlated most strongly with LinkedIn, then conversations with neighbors, then cable television. In the U.S., yield monitors were most closely correlated with YouTube, followed by peer groups, then websites and blogs.
LinkedIn was correlated with the adoption of digital technologies most often in Brazil, while YouTube was more influential in the U.S. Overall, the results showed that social media was more influential among Brazilian farmers than American respondents.
Colussi says these patterns may reflect the demographic makeup of survey respondents. In general, the population matched the farming public in both regions, with Brazilian farmers skewing younger and farming more land than U.S. soybean growers.
“We have demographic differences in our sample that are consistent with the realities of agriculture in both countries,” Colussi said. “Younger people and older generations have different habits in terms of which communication channels they rely on. Regarding tech adoption, we know that older people are sometimes more traditional in terms of risk while younger farmers are sometimes more open to adopting new technologies.”
The study could inform the tactics tech companies use to reach potential customers and increase the overall uptake of digital tools. “With a clearer understanding of the role of communication in farmers’ technology adoption, it should be easier to address the persistent lack of understanding surrounding smart farming technologies in agriculture and consequent low adoption rates.”
It won’t be as simple as advertising on LinkedIn in Brazil or YouTube in the U.S. That’s because, Colussi says, technology adoption is a complex process that occurs over time.
“Let's say I'm a farmer, and I see information about a new chemical or a new machine show up on my Instagram or LinkedIn feed. I might think, ‘Hmm, what is that?’ It’s not likely I will decide to put my money in this machine solely because I saw it in a reel, but I might be curious. Then later, I might see it again while watching an advertisement on television or reading a magazine. It might reinforce that curiosity, and that might make me decide to talk with my neighbors, retailers, or specialists,” Colussi said. “It is important to understand that every channel plays a different role in the diffusion of an innovation.”
The study, “A Comparative Study of the Influence of Communication on the Adoption of Digital Agriculture in the United States and Brazil,” is published in Agriculture [DOI: 10.3390/agriculture14071027]. Authors include Joana Colussi, Steve Sonka, Gary Schnitkey, Eric Morgan, and Antônio Padula.
Journal
Agriculture
DOI
Disaster plant pathology: solutions to combat agricultural threats from disasters
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Key recommendations in disaster plant pathology for designing pathogen and pest management systems before crises and iteratively improving them during and after crises. (Image © The American Phytopathological Society)
view moreCredit: The American Phytopathological Society
An often-overlooked component of natural and human-driven disasters is their potential to affect plant health and thus food security at domestic and international scales. Most disasters have indirect effects on plant health through factors such as disruptions to supply chains and damaged infrastructure, but there is also the potential for direct effects from disasters, such as pathogen or vector dispersal caused by floods, hurricanes, and human migration. These occurrences are rarely isolated and instead often occur simultaneously. We have seen examples of the concurrence of disasters in recent history through events such as market disruptions in the early days of the COVID-19 pandemic; the intense wildfires, hurricanes, and tornadoes that have ravaged in the years since 2020; the Ukraine-Russia war disrupting the global wheat supply; and so on.
The impacts of natural disasters on plant health can be seen after events such as when the soybean rust pathogen, Phakopsora pachyrhizi, was first detected in the United States in Louisiana shortly after Hurricane Ivan in 2004. Hurricane Ivan moved north from the lower Caribbean near Colombia and is believed to have moved spores into the United States. Insect pests can also travel long distances during strong wind events. Bean golden yellow mosaic virus was likely introduced to Florida by Hurricane Andrew in 1992, which carried viruliferous whiteflies from the Caribbean islands. Bean golden yellow mosaic virus caused the reduction or collapse of bean production the following year and became established in Florida. Wildfires also pose a significant risk for the spread of plant pathogens. Fire can damage forest ecosystems, leaving gaps that become habitats open to colonization by invasive pests and pathogens. For example, wildfires in California devastated the coastal mountain range, and new plantings to restore these areas were unknowingly contaminated with Phytophthora tentaculata, a quarantined pathogen that can cause root and crown rot. This has led to more complications, as forest managers now need to control both introduced diseases and future wildfires.
Human-driven disasters, such as armed conflicts, can also create conditions that are favorable to the spread of plant pathogens, leading to devastating consequences for crop production, food security, and overall instability in affected regions. Unrest may force farmers to rely on poor-quality seed with higher risk of disease, resulting in low yields. The current war in Ukraine is an example of how all countries are vulnerable to armed conflict, which not only leads to crop loss and disease spread but also disrupts the global exchange of commodities. The invasion of Ukraine disrupted the global wheat supply and caused a 50% increase in global fertilizer prices due to Russia's significant role as a supplier, accounting for 13% of the world's fertilizer production. In the twenty-first century, poverty, political unrest, and inefficient regulation have significantly influenced the development of major plant disease epidemics.
With the increase in frequency and severity of these disasters, a cross-disciplinary team of researchers and humanitarian experts from the United States, Benin, Ecuador, Kenya, the Netherlands, Peru, Tanzania, and Thailand and led by Berea Etherton from the Garrett Lab (www.garrettlab.com) at the University of Florida, Gainesville, published “Disaster Plant Pathology: Smart Solutions for Threats to Global Plant Health from Natural and Human-Driven Disasters” in the journal Phytopathology. The framework highlighted in the article by Etherton et al. provides a multidisciplinary perspective on current threats and solutions to plant health and food security, encompassing the risk from environmental factors such as climate change, while also including factors such as political instability and war. The international team utilized the One Health framework, which addresses the interconnections among human, animal, plant, and environmental health. Disaster plant pathology provides a framework focusing on the impact of disasters on plant health, plant pathogens, and agricultural systems for tailored solutions and informed decision making. This framework promotes interdisciplinary collaboration, making it of common interest for communities of plant pathologists, humanitarian groups, economists, computer scientists, meteorologists, and sustainable development strategists.
Disaster plant pathology offers solutions through “smart agriculture.” Utilization of the robust capabilities of artificial intelligence (AI) can provide early warning information systems, risk assessment, crop monitoring, supply chain optimization, decision-support, real-time monitoring, and resilience strategies. There is potential for farmers and agricultural authorities to use these tools to make informed decisions and facilitate recovery efforts, thus minimizing the impact of disasters on agricultural systems. Through the integration of satellite imagery, weather data, disease incidence reports, early warning systems, and other relevant information, these models can identify patterns and predict the trajectory of pathogen movement. Farmers and agricultural authorities can use these models to take preventive measures in areas at high risk of infection, effectively managing the spread of disease and accelerating recovery.
The interactions among natural and human-driven disasters, plant disease, and global food security are critical concerns that demand expertise and knowledge from scientists working in disaster plant pathology. Disaster plant pathology recommends actions for improving food security before and following disasters, including (i) strengthening regional and global cooperation, (ii) capacity building for rapid implementation of new technologies, (iii) effective clean seed systems that can act quickly to replace seed lost in disasters, (iv) resilient biosecurity infrastructure and risk assessment ready for rapid implementation, and (v) decision support systems that can adapt rapidly to unexpected scenarios.
Through predictive analyses, early warning systems, and real-time crop monitoring, humanitarian aid and governmental interventions can help to ensure the quality and safety of agricultural production for growers. The experts behind disaster plant pathology hope this framework incites collaboration at an international scale. Lead author Berea Etherton said: “Our team of global researchers and humanitarian experts synthesized current knowledge about disaster effects and strategies for planning and response. We developed this new perspective, disaster plant pathology, so that others working to protect plant health and food security can build on it.” These intricate relationships require global cooperation, and in the face of climate change and geopolitical complexities, a collective and proactive response is needed to protect plant health.
For additional details on the disaster plant pathology framework, read “Disaster Plant Pathology: Smart Solutions for Threats to Global Plant Health from Natural and Human-Driven Disasters” published in the Key Challenges focus issue (volume 114, number 5) of Phytopathology.
The number of billion-dollar disasters in the United States
The number of billion-dollar disasters in the United States, adjusted for inflation, as reported by the National Centers for Environmental Information (NCEI), has been increasing since the 1980s. (Data from Smith, A. B. 2023. 2022 U.S. billion-dollar weather and climate disasters in historical context. NCEI. Climate.gov. Image © The American Phytopathological Society)
Credit
The American Phytopathological Society
Follow the authors on X
Berea Etherton (@Berea_Etherton), Karen Garrett (@Garrett_Lab), Robin Choudhury (@rob_choudhury), Jorge Andrade-Piedra (@jandrade2221), Kwame Ogero (@OgeroKo), Israel Navarrete (@isranavarrete_)
Institutions: University of Florida Plant Pathology Department (@UFPlantPath), University of Florida Global Food Systems Institute (@UF_IFAS_GFSI), CGIAR (@cgiar), International Potato Center (@cipotato), Alliance of Bioversity International and the International Center for Tropical Agriculture (@BiovIntCIAT_eng)
About Phytopathology
For over 100 years Phytopathology® has been the premier international journal for publication of articles on fundamental research that advances understanding of the nature of plant diseases, the agents that cause them, their spread, the losses they cause, and measures used to control them. Articles are characterized by their novelty, innovativeness, and the hypothesis-driven nature of their research.
Journal
Phytopathology
Article Title
Disaster Plant Pathology: Smart Solutions for Threats to Global Plant Health from Natural and Human-Driven Disasters
Pixels to pasture: how AI can help farmers predict their pasture
The Alliance of Bioversity International and the International Center for Tropical Agriculture
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“We want a farmer to locate their farm on a platform and check the quantity and quality of their forage,” say researchers.
view moreCredit: Isabela Salazar
Researchers from the Alliance of Bioversity International and CIAT have paved the way for farmers (from small-holders to big ranchers) information about the quantity and quality of their grazing pastures, right there on their smartphone.
In 2020, global agricultural emissions were 16 billion tonnes of carbon dioxide equivalent, according to the Food and Agriculture Organization of the United Nations (FAO) and other FAO data shows that cattle - including meat and milk - contribute around 3.8 billion tonnes of carbon dioxide equivalent. Increasing the efficiency and output of cattle grazing (like increasing milk production or a larger number of animals) without adding a larger environmental footprint is a key goal in reducing these emissions.
In a 2024 paper Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands published in the international journal Remote Sensing Applications: Society and Environment, researchers from the University of Glasgow and the Alliance of Bioversity International and CIAT lay out a how-to guide on taking information from satellites and using predictive models to evaluate grazing pastures in terms of quantity (how much biomass) and quality (crude protein, digestibility, and ash content).
Juan Andrés Cardoso Arango, a co-author of the paper and a plant eco-physiologist focusing on tropical forages at the Alliance of Bioversity International and CIAT, explains that today, analyzing all the factors that determine quantity and quality are hard to scale: using a small drone, you can only sample nine hectares or so at a time and even less with hand-held instruments.
“In some parts of Colombia, you can have properties of 3000 hectares,” he says, adding that this is one of the reasons the researchers developed a “scale neutral” system, that can gather data via satellite, square kilometers at a time, but also be just as useful to a farmer with just one hectare.
Cardoso explains that free-to-use satellite imagery databases and technological advances in AI-driven processing have “democratized” this analysis.
“When I started in 2018, nobody knew about machine learning, now you can have this information faster than before,” he says.
AI-driven Prediction
Diana María Gutiérrez Zapata, a senior research associate at CIAT, a data analysis specialist and co-author on the paper explains that predicting pasture productivity and quality using remote sensing is challenging due to the many influencing factors and data limitations.
“By better characterizing productive systems and capturing more accurate data on control and response factors, there is significant potential to develop high-performance predictive models,” she says, “These models can underpin digital tools to support strategic decision-making, enabling farmers to optimize pasture management and better manage risks (such as water scarcity and low-quality forage) within their production systems.”
Brian Barrett, a reader (associate professor) at the University of Glasgow, in Scotland, UK, a co-author of the study and an expert in spaceborne sensors, explains that in 2017, he, Cardoso and colleagues met and started to discuss the potential use of remote sensing or earth observation data and machine learning approaches for estimating forage characteristics across different climates, including intensive and extensive pastures.
“Important to us was the connection with smallholder farmers and how we could develop something that would provide useful information for them and ultimately lead to improved decision making and management,” Barrett says.
The Future
Cardoso explains that the long-term aim is to develop a model with a user interface as easy to use as Google Maps.
“We want a farmer to locate their farm on a platform and check the quantity and quality of their forage,” he says.
Gutiérrez explains that in a changing climate, having timely information about expected pasture production or quality is crucial for risk management.
“By being more aware of the risks associated with pasture management decisions, farmers can make better-informed choices regarding production, use, and conservation,” she says, “This not only benefits farmers by optimizing resource use but also positively impacts the environment by reducing emissions and waste, addressing various issues aligned with the Sustainable Development Goals.”
Barrett explains that in the future the team would like to develop the approach to allow not only a better idea of available forage resources but also how pastures would react to different management and climate scenarios.
“As most forest loss globally (~75%) has been driven by conversion to agriculture, and given increasing global populations, and the associated increases in food consumption and resource use, it is critical that we find new ways to increase food production while preserving our remaining forests,” he says, adding that incorporating technologies such as satellite data and advanced machine learning approaches can lead to more efficient and profitable farming, and improved food system sustainability.
- Learn more by reading the paper: https://doi.org/10.1016/j.rsase.2024.101282
Journal
Remote Sensing Applications Society and Environment
Article Title
Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands
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