































   Advancements in Agricultural Development 
  Volume 6, Issue 4, 2025 
  agdevresearch.org 

 

1. Ginger Orton, Assistant Professor, University of Georgia, 180 E. Green St., Athens, GA, 30602, ginger.orton25@uga.edu, 

 https://orcid.org/0009-0005-4582-7700   
2. Chin-Ling Lee, Assistant Professor, University of Georgia, 405 College Station Rd., Athens, GA, 30602, cllee@uga.edu,      

 https://orcid.org/0000-0001-7752-6113 
 

 
20 

 

Regional Dynamics of Precision Agriculture Adoption and 
Knowledge Transfer: Insights from Georgia Extension Agents 

 
G. Orton1, C.-L. Lee2 

 
 

Article History 
Received: June 26, 2025 
Accepted: October 28, 2025 
Published: November 8, 2025 
 
 
Keywords 
uses and gratifications; Diffusion of 
Innovations; tailored outreach; 
knowledge transfer; SDG 12: 
Responsbile Consumption and 
Production  
  

Abstract 
The Cooperative Extension System plays a vital role in disseminating 
innovative knowledge from the University to public decision makers. 
However, there is a lack of studies on the dynamics of knowledge transfer 
from Extension agents to farmers, especially in the Southeastern U.S. in 
relation to precision agriculture adoption. This study aims to examine the 
dynamics of farmers’ engagement with Extension agents in the state of 
Georgia, particularly concerning the adoption of precision agriculture. 
This study conducted a survey of Agriculture and Natural Resource 
Extension agents in Georgia; 84 agents were surveyed. The findings 
indicated that more than half of the Extension agents reported that they 
had been approached by farmers about precision agriculture-related 
Extension services in the past two farming seasons. The statewide 
average precision agriculture adoption rate was 43.78%, but adoption 
rates varied by geographic region, with the southern part of Georgia 
reporting a higher adoption rate than the northern part of the state. The 
findings offer insight into the information-seeking relationship between 
change agents and farmers in a precision agriculture context by focusing 
on the perspective of Extension agents, laying the groundwork for future 
research to explore the complementary viewpoint from targeted current 
and potential precision agriculture adopters.   

mailto:ginger.orton25@uga.edu
https://orcid.org/0009-0005-4582-7700
mailto:cllee@uga.edu
https://orcid.org/0000-0001-7752-6113


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Introduction and Problem Statement 
 
The Cooperative Extension System (CES) plays a vital role in disseminating innovative research 
from land-grant universities to the public, particularly agricultural producers for whom the 
institution was founded. Through outreach, Extension seeks to make innovative knowledge 
relevant, beneficial, and actionable to the public (National Institute of Food and Agriculture, 
2025). Across the last 20 years, precision agriculture (PA) technology has increased in salience 
across scientific and industry domains. While multiple definitions of PA exist, we draw from 
DeBoer and Erickson’s (2019) definition which states, “Precision agriculture is a management 
strategy that uses electronic information and other technologies to gather, process, and 
analyze spatial and temporal data for the purpose of guiding targeted actions that improve 
efficiency, productivity, and sustainability of agricultural operations” (p. 1553). PA technologies 
can generally be grouped into three categories: tools for collecting data (such as soil and yield 
monitors, sensors, or drones), tools for supporting decision-making (such as mapping software), 
and tools for adjusting inputs (such as variable-rate application systems) (Thompson et al., 
2018). Together, they support the precise application of inputs such as water, fertilizer, and 
herbicides and provide economic and environmental benefits, thus benefiting both the farmer 
and society at large, as opposed to traditional whole-field applications (Getahun et al., 2024; 
Sanyaolu & Sadowski, 2024; Šarauskis et al., 2022). The economic savings of precise application 
are particularly important in a time of increasing input costs (Athearn, 2025).  
 
Knowledge transfer from Extension agents to farmers is a two-way, symbiotic relationship 
essential for effective dissemination of beneficial innovations. Without meaningful interactions 
between the two, Extension’s mission cannot be fulfilled, and farmers may not receive insight 
that can improve their operations. Extension agents have historically been trusted and sought 
by farmers across topics (Camillone et al., 2020; Heaney-Mustafa et al., 2018; Orem et al., 
2024), however, only a small pool of research has investigated the dynamics between American 
farmers and Extension agents in the context of PA (Lee et al., 2023; Looney et al., 2022). 
Specifically, research has called for a more granular examination of engagement and adoption 
surrounding PA in an American context (Júnior et al., 2024). As an example, the most recent 
2022 United States Department of Agriculture (USDA) Census broadly measured adoption as 
whether farmers use PA, but it did not investigate which practices farmers use (USDA, 2025). A 
deep dive into regional PA adoption patterns and the dynamics between farmers and Extension 
agents can contribute to improving engagement and overall adoption of PA. 
 

Theoretical Framework 
 
To frame this investigation, constructs from two theories were used: Rogers' (2003) Diffusion of 
Innovation (DOI) and Katz et al. (1973) Uses and Gratifications. Together, these theories help 
describe the way individuals, in this case farmers, seek information about novel technologies.  
 
Rogers’ (2003) DOI theory explores how innovations like PA diffuse through society and 
highlights the role of change agents in this process. Change agents, such as Extension agents, 

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influence the adoption of innovations within social system groups, like farmers. Agents 
accelerate how the technology’s applications and benefits spread among potential adopters.  
 
However, change agents are only effective when those receiving knowledge seek and receive 
meaningful information from the agent. These interactions are represented in Katz et al.’s 
(1973) uses and gratifications theory, which helps explain how and why individuals, such as 
farmers, engage with information sources, such as Extension agents. Information seekers play 
an active role in choosing their knowledge sources and seek information to fulfill their needs. As 
Extension agents are a primary information source for farmers, it is essential that Extension 
agents be prepared to meet farmers’ information needs, including understanding the benefits 
of PA and farmers’ adoption motivations to best leverage these concepts when engaging with 
farmers. 
 

Purpose 
 

This study sought to understand the dynamics of farmers’ engagement with Extension agents in 
the state of Georgia, particularly concerning the PA knowledge transfer channels. The 
objectives of this study were to investigate the role of Extension agents in facilitating PA 
adoption by describing (a) the current state of farmers’ engagement with Extension agents 
about PA information delivery methods, and (b) Georgia’s regional PA adoption. These findings 
provide an updated and alternative description of PA adoption and information seeking in 
Georgia, which can improve targeted outreach determined to increase both meaningful 
engagement between farmers and Extension agents and adoption of PA.  
 

Methods 
 

To address the study's objectives, we conducted a census-style survey following Dillman et al’s 
(2014) tailored design method. The instrument consisted of 13 questions that addressed the 
competencies required for PA knowledge transfer and the usage of existing communication 
channels. The data presented in this article were analyzed from a set of questions related to the 
usage of existing communication channels, aiming to address the research questions of this 
study. The target population included 127 Agriculture and Natural Resources (ANR) Extension 
agents affiliated with the University of Georgia Cooperative Extension. These individuals were 
selected because their work involved PA or direct interaction with farmers who either currently 
use or have the potential to adopt PA technologies. Recruitment was supported by the 
administrative teams of UGA Extension’s four districts. 
 
The instrument asked agents a series of questions to describe their clientele’s information 
seeking frequency and production context related to PA. The item development was informed 
by information-seeking constructs in two theories: Uses and Gratification (Katz et al., 1973) and 
Diffusion of Innovation (Rogers, 2003) as well as the core components of the Extension mission 
to provide information dissemination to farmers. Additionally, the survey instrument 
underwent iterative rounds of expert review by a University of Georgia ANR specialist and an 

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Extension faculty member to assess content clarity and face validity. Information seeking was 
measured using a categorical question asking, “Thinking back to the past two planting and 
harvest seasons, how frequently have producers come to you with precision agriculture 
questions, discussions, or challenges?” with the options of never, once 3-5 times, 5-10 times, 
more than 10 times. The question focused on just the past two planting and harvest seasons to 
provide a consistent timeframe for each respondent in a relatively easy to recall span. 
Respondents were also asked to enter a numeric percentage of organic and conventional 
clientele as well as the percentage of clientele who currently use PA (0% to 100%). Additionally, 
we asked them to rank the top five commodity groups they directly work with as well as the 
percentages of each type of Extension clientele (e.g., crop farmers, homeowners, etc.). These 
last three questions were included to provide more context to the information-seeking 
landscape. The types of PA adopted in their regions were measured qualitatively by asking 
them to list examples. Given the qualitative nature of the data and the diversity in terminology 
used by respondents, we used a thematic analysis approach to identify the types of PA 
organically from the data while referencing common PA technology in the literature.    
 
Surveys were administered both in print and electronically. In two Extension districts, 
questionnaires were distributed in person during ANR Extension Agent Update meetings. The 
remaining two districts utilized an online version due to the absence of scheduled meetings 
during the data collection window. Data were gathered in February 2025. A total of 87 agents 
completed the survey, yielding a response rate of 69%. After excluding three incomplete 
submissions, the final dataset included 84 valid responses (N = 127; n = 84), with a satisfactory 
response rate of 66% (Fincham, 2008). The majority of respondents were male (n = 55; 65%) 
with a mean age of 41.21 years (SD = 13.35), ranging from 22 to 70 years. The participants 
reported an average of 10.67 years of experience in Extension roles (SD = 10.9), ranging from 
0.5 to 48 years.  
 
Data analysis was conducted using SPSS version 29 and Excel. To analyze the qualitative 
responses, we used an inductive coding approach to create categories that could 
comprehensively capture the practices mentioned. The codebook underwent two rounds of 
revisions until it was established as reliable, meaning both authors categorized all mentioned 
practices similarly under each code. 
 

Findings 
 
The first research objective sought to describe how frequently Extension agents were contacted 
about PA. Participants selected how many farmers contacted them regarding PA from the 
options shown in Table 1.  
 
 
 
 
 

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Table 1  
 
Frequency of Farmers Seeking PA Information During the Last Two Planting and Harvesting 
Seasons (n = 79)  
Frequency Option f % 
None 23 29.1 
1 or 2 producers 20 25.3 
3-5 producers 19 24.1 
6-10 producers 9 11.4 
More than 10 producers 8 10.1 

 
The second research objective was to describe the current state of PA technologies were 
implemented in each agent’s region, as well as the percentage of farmers using PA in each 
county. Fifty participants (59.5%) were able to list examples of PA. Table 2 reports the 
frequency of practices mentioned by participants. 
 
Table 2  
 
Frequency of Applied Technologies in Georgia (n = 50) 
Technology f % 
GPS 22 46 
Auto-navigate 16 32 
Sensors – moisture 14 28 
Data collection & monitoring 14 28 
Drone – general 13 26 
VRT 12 24 
Equipment 11 22 
Irrigation 10 20 
Mapping 9 18 
Yield monitors 5 10 
Drone – spray 5 10 
GIS 2 4 
Sensors – general 2 4 

Note. Participants could list multiple technologies; therefore, the total number of technologies 
exceeds the number of respondents.  
 
Respondents were also asked to report the perceived percentage of farmers in their district 
who were currently using PA. An average adoption rate of 43.78% was reported (Min = 0% 
adoption, Max = 100% adoption). The most frequently reported percentage of adoption was 
10%, which represented 9 participants (10.7%), followed by 7 (8.3%) participants who reported 
90% of their constituents used precision agriculture.  Figure 1 maps the percentage density of 
PA adoption by county in Georgia.   
 

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Figure 1  
 
PA Adoption Percentage Mapped by County in Georgia with Regional Distinction 

 
Note. NW = northwest region, NE = northeast region, SW = southwest region, SE = southeast 
region; Regions based on CES classification in Georgia; All data were mapped by zip code, which 
may span county lines typically used by CES to define regions in Georgia 
 
The agent’s clientele was examined in terms of frequencies of organic and conventional 
production, the most present commodities, and the type of clientele. Most production was 
conventional (85%), with 15% classified as organic production. Table 3 represents the mean 
rankings of the most present commodities in respondents’ districts. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Table 3  
 
Extension Agent’s Clientele Commodity Categorizations (n = 82) 

 N M SD 
Peanuts 39 1.90 1.14 
Nursery 23 2.17 1.34 
Cotton 40 2.30 1.24 
Vegetables 36 2.42 1.38 
Poultry 13 2.77 1.83 
Hay 58 2.78 1.46 
Fruit 39 2.87 1.44 
Beef 49 2.98 1.55 
Tobacco 3 3.00 2.00 
Corn 36 3.08 1.18 
Aquaculture 3 3.33 2.08 
Horses 11 3.45 1.51 
Soybeans 13 3.62 1.61 
Sheep 8 3.88 1.46 
Christmas Trees 6 4.00 .89 
Grains 5 4.20 .45 
Hogs 2 4.50 .71 
Dairy 3 4.67 .58 

Note. Categories are sorted from the lowest mean to the highest mean, because the mean 
score represents a rank, with 1 being the highest, most frequent rank, and 5 being the least 
frequent ranking, indicating a lower frequency.  
 
The Extension agents identified all seven client categories as targets for their services, with crop 
growers being the most prevalent. Seventy-two respondents reported that, on average, 40.37% 
of their clients were crop growers, followed by homeowners (i.e., those seeking services related 
to maintaining, improving, or managing their home and landscape) with a percentage of 23.7%. 
Table 4 listed the most common constituent classifications of Extension agents’ clientele.   
 
Table 4  
 
The Most Common Constituent Classifications of Extension Agents’ Clientele (n = 82) 
Classification N M SD 
Crop Growers 72 40.37 31.66 
Livestock Producers 74 18.58 16.38 
Foresters 56 6.27 6.43 
Homeowners 76 23.70 20.51 
Hobby Gardeners 70 10.94 12.15 
Small Businesses 61 7.97 11.21 
Educators 62 6.11 7.83 
Note. Mean represents the average percentage of each classification reported.  
  

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Conclusions, Discussion, and Recommendations 
 
The DOI framework emphasizes the role of change agents in the adoption process through their 
information exchange relationship (Rogers, 2003). Extension agents are prime candidates to 
foster PA adoption, and more than half of the ANR Extension agents (n = 56, N = 79, 70.9%) 
responded that they had been approached by at least one farmer about PA-related Extension 
services in the last two farming seasons. The uses and gratifications theory helps explain why 
individuals seek and engage with certain information sources (Katz et al., 1973), and our 
findings suggest farmers are choosing to engage with Extension agents about PA to meet their 
information needs. Because of this existing relationship, Extension agents must be provided 
with continued support to ensure they are prepared with the relevant knowledge and skills to 
meet farmers’ information needs (Luck et al., 2015). Farmers need support and assistance to 
adopt the PA technologies, and Extension agents are uniquely positioned to provide this 
support (Lee et al., 2021). Additionally, Extension agents must be prepared to meet farmers’ 
information needs when farmers seek PA-related information and be ready to support PA 
integration. Areas of high adoption density in the southern part of the state could also be 
promising places to start and may serve as hubs to drive adoption expansion in neighboring 
counties with large-scale production and hence increased propensity to adopt. Extension 
agents will need to collaborate with farmers and Extension agents beyond their counties to 
increase both adoption and meaningful knowledge transfer from the CES to the farmer. 
 
The constituent classification results revealed that Georgia Extension agents work with diverse 
groups, primarily homeowners, livestock producers, crop growers, and hobby gardeners. This 
finding should be maintained when executing professional development and resource 
allocation. Although PA is increasing in salience for some constituent groups (e.g., crop growers, 
foresters, livestock producers), research also shows these groups tend to seek information from 
private consultants and agribusiness dealers (Erickson & Lowenberg-DeBoer, 2024), which 
illuminates the need for both capacity-building among Extension agents and an increased 
awareness among producers of the potential for agents to meet their PA needs. The findings 
highlight the importance of ensuring Extension agents are equipped with foundational PA 
knowledge to respond to the demand from this group of clientele effectively. 
 
To further describe the potential observability of adoption (Rogers, 2003), we also sought to 
understand the current PA adoption rates in Georgia. Of the 84 respondents, 50 (59.5%) listed 
one or more examples of PA being used in their district. When asked which practices were 
being used, almost half of our respondents (n = 24, 48%) listed GPS or GIS technology, followed 
in frequency by auto-navigate technology (n = 16, 32%) and moisture sensors and data 
collection and monitoring (each with n = 14, 28%). Drones, VRT, equipment such as planters, 
and irrigation were each mentioned more than 10 times. It should be noted that the USDA 
Census only broadly measures PA use as yes/no, but has not yet measured the types of 
practices used, and no other studies have investigated Georgia’s PA use on the county level. 
Therefore, the documentation of which practices are being used in Georgia provides novel 
insight into adoption. However, the variability and limited specificity in Extension agents’ 

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responses regarding PA technologies suggest a potential gap in their familiarity with commonly 
defined categories in the literature and those used by industry providers (Erickson & 
Lowenberg-DeBoer, 2024). Many responses from Extension agents were broad or lacked detail, 
which underscores the need for targeted educational efforts to enhance Extension agents’ 
knowledge of PA tools and terminology. 
 
Across the state, an average adoption rate of 43.78% was reported. However, when looking at 
the county level, patterns emerge. The southern half of the state had higher levels of reported 
adoption than the northern half, which aligns with findings from the 2022 USDA Census (USDA, 
2025). This finding is further supported by the fact that most of the large-scale peanut and 
cotton production takes place in the southern half of the state (USDA, 2025), aligning with 
studies finding high-density commodities adopt PA technologies more rapidly 
(Schimmelpfennig & Lowenberg-DeBoer, 2020), likely because their benefits are more visible to 
farmers (Rogers, 2003), encouraging adoption. Our findings do differ from the 7% PA use rate 
reported in the 2022 USDA Census, which may suggest Extension agents perceive there is more 
adoption due to the nature of their clientele who may be early adopters or have high 
information seeking behavior. Additionally, while PA can be used on both organic and 
conventional operations, most agents reported they worked with conventional clientele. The 
2022 USDA Census reported the number of organic farms in Georgia had steeply declined by 
19% from 2017 to 2022, with only 105 certified organic farms reported (<1%) (USDA, 2025). Our 
data showed Extension agents perceived a higher rate of organic production (15%), 
demonstrating organic farmers may be more engaged with Extension and therefore perceived 
as higher in number.  
 
This study offers insight into the change agents and information-seeking relationship by 
focusing on the perspective of the Extension agent, laying the groundwork for future research. 
Notably, our instrument could have benefited from additional quantitative items to increase its 
reliability and better capture the role Extension agents play in the knowledge transfer process.  
While our qualitative measurement of which PA technologies are being used is a place to start, 
future inquiry should extend these findings to understand which technologies agents are 
confident in and which would benefit from further training.  We also recommend that future 
research should build upon this foundation to explore the complementary viewpoint by 
measuring information-seeking behavior and motivations using a farmer sample to provide a 
more comprehensive understanding of adoption challenges and information dissemination 
process. Additional qualitative inquiry into the nature of the interactions from each viewpoint 
would also contribute meaningful insight. 
 
  

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Acknowledgments 
 

Funding Information: This study is based on work supported by the Research Capacity Fund 
(Hatch) project, National Institute of Food and Agriculture, U.S. Department of Agriculture, 
number 7009935. 
 
Conflict of interest: There are no conflicts of interest. 
 
Previous Dissemination: This scholarship has not been previously reported. 
 
Artificial Intelligence: AI tools were not used in this study. 
 
Author Contribution Statement: G. Orton – methodology, formal analysis, investigation, data 
curation, writing – original draft, review, & editing, visualization; C.-L. Lee – conceptualization, 
methodology, formal analysis, investigation, writing – review & editing, supervision.  
 
 

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© 2025 by authors. This article is an open access article distributed under the terms and conditions of 
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