































   Advancements in Agricultural Development 
  Volume 5, Issue 4, 2024 
  agdevresearch.org 

 

1. Henry O. Akwah, Precision Agriculture Instructor, 308 ASB, University of Arkansas Community College – Batesville, 2005 

White Drive, Batesville, AR 72501, henryofosu.akwah@uaccb.edu,  https://orcid.org/0009-0007-3164-3614  
2. Donald M. Johnson, University Professor, University of Arkansas, E108 AFLS Building, Fayetteville, AR 72701, 

dmjohnso@uark.edu,  https://orcid.org/0000-0003-2592-654X  
3. George W. Wardlow, Professor and Head, University of Arkansas, E108 AFLS Building, Fayetteville, AR 72701, 

wardlow@uark.edu,  https://orcid.org/0000-0001-5701-8250  
4. Cengiz Koparan, Assistant Professor, University of Arkansas, E108 AFLS Building, Fayetteville, AR 72701, 

ckoparan@uark.edu,  https://orcid.org/0000-0003-3940-0055  
5. Aurelie Poncet, Assistant Professor, University of Arkansas, 115 Plant Sciences Building, Fayetteville, AR 72701, 

poncet@uark.edu,  https://orcid.org/0000-0002-0137-3023 
28 

 

Inservice Needs of Selected Arkansas Agriculture Teachers 
Related to Precision Agriculture 

 
H. O. Akwah1, D. M. Johson2, G. W. Wardlow3, C. Koparan4, A. Poncet5 

 
 

Article History 
Received: June 7, 2024 
Accepted: September 16, 2024 
Published: October 18, 2024 
 
 
Keywords 
Borich model; educational needs 
assessment; SDG 4: Quality Education 
  

Abstract 
The purpose of this study was to determine selected Arkansas school-
based agricultural education (SBAE) teachers’ perceptions of the 
importance, ability to teach, inservice needs, and barriers relative to 
incorporating precision agriculture (PA) into their programs. A non-
probability sample (n = 44) of teachers participating in an introductory PA 
workshop completed the survey. Teachers rated each of the  PA 
competencies as being above average or high importance but rated their 
ability to teach each competency as being none or below average. When 
competencies were grouped into seven PA topics, teachers had inservice 
needs for each topic with mean weighted discrepancy scores (MWDSs) 
ranging from 8.16 (guidance and autosteering systems) to 11.81 
(geographic information systems). Years teaching experience, row-crop 
experience, and experience with PA had negligible to substantial negative 
correlations with inservice needs in each PA topic. A majority of teachers 
rated the lack of equipment (86.3%), curriculum materials (84.1%), 
personal knowledge (81.9%), and inservice opportunities (63.7%) as 
being either moderate or serious barriers to incorporating PA into their 
programs. These results indicated a perceived need for inservice 
education in PA, provided insight into priority topics, and identified 
potential barriers to incorporating PA into the curriculum. 

mailto:henryofosu.akwah@uaccb.edu
https://orcid.org/0009-0007-3164-3614
mailto:dmjohnso@uark.edu
https://orcid.org/0000-0003-2592-654X
mailto:wardlow@uark.edu
https://orcid.org/0000-0001-5701-8250
mailto:ckoparan@uark.edu
https://orcid.org/0000-0003-3940-0055
mailto:poncet@uark.edu
https://orcid.org/0000-0002-0137-3023


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Introduction and Problem Statement 
 
Precision agriculture (PA) is a management approach that applies technology to manage spatial 
and temporal variation in crop production with the potential to improve economic and 
environmental sustainability (Association of Equipment Manufacturers, 2023; Oliver et al., 
2013). PA technologies include real-time kinematic GPS for on-the-go position monitoring, 
variable rate technology for application of inputs (seed, fertilizer, and chemicals),  unmanned 
aerial vehicles for digital image acquisition, guidance and autosteering for machine operation, 
yield monitoring and mapping for collecting georeferenced yield data, soil sensing for 
measuring and mapping soil properties, and geographic information systems for storing and 
mapping georeferenced agronomic data (Ess & Morgan, 2017; McFadden et al., 2023; Skouby, 
2017).   

A 2019 survey of 165 primarily Midwestern US farm supply companies (Erickson & Lowenberg-
DeBoer, 2020) found that 92% used real-time kinematic GPS and guidance and autosteering 
systems, 81% used geographic information systems, 40% used unmanned aerial vehicles, and 
27% used soil sensing technology; only 4% reported not using any PA technology. Erickson et al. 
(2018) found a majority of farm supply employers preferred high school or community college 
graduates for PA equipment operator (81%) and technician (59%) positions. 

Despite these PA career opportunities PA is not commonly taught in school-based agricultural 
education (SBAE) programs due to a lack of funding, equipment, teacher knowledge, and 
curriculum materials (Chad, 2022). Therefore, a need existed to determine the specific inservice 
needs of selected Arkansas SBAE teachers related to PA and to determine their perceptions of 
barriers to teaching PA. This data could be used to assist PA implementation. 
 

Theoretical and Conceptual Framework 
 
Shulman’s (1986, 1987) work related to teacher knowledge formed the theoretical framework 
for this study. According to Shulman, effective teaching requires content knowledge (CK) and 
general pedagogical knowledge (GPK), plus a blending of these two types of knowledge into a 
specialized form of teacher knowledge called pedagogical content knowledge (PCK). According 
to Shulman (1987), PCK “is that special amalgam of content and pedagogy that is uniquely the 
province of teachers, their own special form of professional understanding” (p. 8). Shulman 
(1986) stated that PCK involves “the ways of representing and formulating the subject that 
make it comprehensible to others . . . [through] . . . the most useful forms of representation of 
those ideas, the most powerful analogies, illustrations, examples, explanations, and 
demonstrations” (p. 9).  

According to Etkina (2010), “deep content knowledge is a necessary condition for the 
development of PCK” (p. 2). Research in mathematics education (Caparo et al., 2005), science 
education (Etkina, 2010; Neumann et al., 2019), and agricultural education (Rice & Kitchel, 
2015; Wooditch et al., 2018) broadly supports this contention. Grossman et al. (1989) 
highlighted the importance of CK when they stated, “Good teachers not only know their 

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content but know things about their content that make effective instruction possible’’ (p. 24). 
Because CK is essential to the development of PCK, this study focused primarily on evaluating 
teacher’s CK as a precursor to the development of PCK for teaching PA principles, practices, and 
application skills.  

Purpose 
 
The purpose of the study was to determine the inservice needs of selected Arkansas school-
based agriculture education (SBAE) teachers related to Precision Agriculture (PA). Specific 
objectives were to determine: 
1. The perceived importance Arkansas SBAE teachers place on teaching selected PA topics in 

their programs.  
2. Arkansas SBAE teachers’ perceived abilities to teach selected PA topics.  
3. The in-service needs of Arkansas SBAE teachers related to the PA topics as indicated by 

mean weighted discrepancy scores (MWDSs).  
4. The relationship between Arkansas SBAE teacher and community characteristics, and 

teacher PA in-service needs.  
5. Arkansas SBAE teachers’ perceptions of barriers to incorporating PA competencies into the 

curriculum. 
 

Methods 
 
This study employed a non-probability convenience sample (n = 44) of Arkansas SBAE teachers 
participating in a two-hour introduction to PA workshop conducted in July 2023 as part of the 
state agriculture teacher summer conference. Paper surveys were administered immediately 
prior to the workshop and all teachers provided usable data. Because a non-probability sample 
was used, these results should not be generalized beyond the respondents; however, according 
to Johnson and Shoulders (2017), “Studies yielding valid results of interest to the profession 
from a specific group of respondents, regardless of their generalizability, can add to the body of 
knowledge and assist researchers as they design and conduct research” (pp. 310-311).  

The survey instrument contained three sections. The first section listed 29 specific PA 
competencies, grouped into seven PA topics (RTK-GPS, 3 items; variable-rate technology, 5 
items; unmanned aerial vehicles, 4 items; guidance and autosteering, 4 items; yield monitoring 
and mapping, 4 items; soil sensing, 4 items; and geographic information systems, 5 items). Each 
PA topic was described in the instrument prior to each pair of scales. The competencies were 
identified based on previous research (Ess & Morgan, 2017; McFadden et al., 2023; Skouby, 
2017). For each competency, respondents rated (a) the importance of teaching the competency 
to their students, and (b) their current ability to teach the competency. Importance and ability 
were both measured on 1 to 5 Likert scales (1 = no importance/ability, 2 = below average 
importance/ability, 3 = average importance/ability, 4 = above average importance/ability, and 5 
= high importance/ability). The second section listed seven potential barriers to teaching 
precision agriculture (Chad, 2022) measured on a 1 to 4 Likert-type scale (1 = not a barrier, 2 = 

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minor barrier, 3 = moderate barrier, and 4 = serious barrier). The third section contained five 
items related to teacher and community characteristics such as years of teaching experience, 
experience with row crop farming and precision agriculture, school ZIP code, and teacher 
perceptions of their county’s economic dependance on row crop farming.  

The instrument was reviewed by a panel consisting of two university experts in PA and two in 
social science research who were familiar with the research context and objectives; these 
experts judged the instrument to possess face and content validity (Gates, et al., 2018). 
Coefficients of stability, established with 13 preservice agricultural education students who 
completed the instrument twice at a 14-day interval, ranged from .85 to .98 for PA importance 
and from .57 to .93 for PA ability. Reliability for potential barriers was .93. Because inservice 
priorities were identified by PA cluster, not by individual competencies, coefficient alpha 
reliabilities were estimated post-hoc to determine the extent to which teachers’ ratings of 
importance and ability, respectively, were consistent within each PA cluster. Coefficient alpha 
reliability estimates for the summated PA topic importance scales ranged from .86 (unmanned 
aerial vehicles) to .96 (yield monitoring and mapping); reliability estimates for the summated 
PA ability scales ranged from .93 (unmanned aerial vehicles) to .98 (multiple scales). 

Mean weighted discrepancy scores (MWDS) were calculated for each of the seven PA topics 
using Equation 1 based on Borich (1980). 

MWDS = [(ΣI – ΣA) / N] x MIT.     (1) 

Where: MWDS = Mean weighted discrepancy score for the PA topic. ΣI = Sum of the importance 
ratings for all competencies in the PA topic. ΣA = Sum of the ability ratings for all competencies 
in the PA topic. N = Number of competencies in the PA topic MIT = Mean importance rating for 
all competencies in the PA topic. 
 
Based on the 1 to 5 scales used to measure both importance and ability, each MWDS could 
theoretically range from −4.0 to 20.0, with larger positive scores indicating greater inservice 
needs (Borich, 1980). Because each MWDS was based on summated importance and ability 
scales for each PA topic, the mean importance scores were considered interval level 
measurements (Batterton & Hale, 2017; Dillman et al., 2014; Norman, 2010), overcoming the 
criticisms of MWDS calculated on individual ordinal level items (Narine & Harder, 2021).  
 

Findings 
 
Among the 44 respondents, teaching experience ranged from 0 years (two newly hired 
teachers) to 42 years (1 teacher), with a median of 12.0 years and an interquartile range of 22.0 
years. A majority of respondents (88.6%) reported no experience with row crop farming or with 
PA technologies (77.3%). Analysis of school ZIP codes indicated that 20 (45.5%) teachers taught 
at schools located in the two primary row crop areas of Arkansas, the Arkansas River Valley and 
the Arkansas Alluvial Plain while the remaining teachers (54.5%) taught at schools located in 
areas primarily characterized by poultry, timber, and livestock production (Williams, 2023). 

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When asked to indicate the extent to which their county was economically dependent on row 
crop agriculture, 43.2% indicated “not at all dependent,” 27.3% indicated “somewhat 
dependent,” and 29.6% indicated “highly dependent.”  

The first objective was to determine the importance teachers placed on teaching the PA topics 
and competencies (see Table 1). Using the vague descriptors suggested by Lindner and Lindner 
(2024) for summated 5-point Likert scales, teachers rated the importance of teaching each PA 
topic as being of “above average” importance. Based on the mean topic importance ratings, the 
unmanned aerial vehicle and geographic information systems were the most important topics 
while the yield monitoring and mapping and guidance and autosteering topics were the least 
important. Only one individual competency (configure a yield monitoring system) was rated as 
being of no or below average importance by 10% or more of the teachers; conversely, each of 
the 29 competencies was rated as being of above average or high importance by between 
70.5% and 93.3% of the teachers. 
 
Table 1 
 
Arkansas SBAE Teachers’ Perceptions of the Importance of PA Topics and Competencies 
 Importance   

 
PA Topic 
Competency 

None or 
below 

averagea 
(%) 

 
 

Average 
(%) 

Above 
average 
or highb 

(%) 

 
 
 

Mc 

 
 
 

SD 
Unmanned aerial vehicles (UAVs)    4.40 0.59 
Use computer program to analyze digital 

UAV images 
0.0 6.8 93.2   

Develop a “mission plan” to fly UAV 0.0 9.1 90.9   
Manually fly a UAV 2.3 6.8 90.9   
Select correct camera for specific UAV 

application 
2.3 9.1 88.6   

Geographic information systems (GIS)    4.39 0.62 
Create a field map 2.3 4.6 93.2   
Determine sources of geographic data 0.0 9.1 90.9   
Edit a field map 2.3 6.8 90.9   
Describe coordinate system used in GIS 2.3 6.8 90.9   
Import data into a GIS program 0.0 15.9 84.1   
Soil sensing (SS)    4.23 0.65 
Select the correct sensor for a specific task 2.3 9.1 88.6   
Use a computer program to analyze soil 

sensor data 
0.0 13.6 86.4   

Determine when to use proximal and 
remote sensing 

2.3 13.6 84.1   

Determine when to use grid vs. zone 
sampling 

2.3 15.9 81.8   

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 Importance   

 
PA Topic 
Competency 

None or 
below 

averagea 
(%) 

 
 

Average 
(%) 

Above 
average 
or highb 

(%) 

 
 
 

Mc 

 
 
 

SD 
Variable rate technology (VRT)    4.20 0.0.74 
Identify the primary components of VRT 

system 
2.3 9.1 88.6   

Operate VRT equipment in the field 2.3 9.1 88.6   
Identify common sensors used in VRT 2.3 11.4 86.4   
Configure a variable rate controller 2.3 13.6 84.1   
Develop a prescription application map 4.6 15.9 79.5   
Realtime kinematic GPS (RTK-GPS)    4.17 0.66 
Pair tractor or implement receiver (rover) 

with RTK base station 
0.0 15.9 84.1   

Explain basic operating principles of RTK-
GPS 

0.0 18.2 81.8   

Identify sources of position error in RTK-
GPS 

0.0 22.7 77.3   

Yield monitoring and mapping (YMM)    4.07 0.0.87 
Identify common sensors used in YMM 

systems 
6.8 13.6 79.6   

Configure a yield monitoring system 11.4 9.1 79.6   
Calibrate a grain moisture sensor 6.8 15.9 77.3   
Identify primary components of a YMM 

system 
9.1 15.9 75.0   

Guidance and autosteering systems (GAS)    3.98 0.88 
Operate a tractor in the field with lightbar 4.6 15.9 79.5   
Operate a tractor in the field with 

autosteering 
6.8 15.9 77.3   

Set an AB line and swath width for 
autosteering 

6.8 15.9 77.3   

Set an AB line and swath width for lightbar 
guidance 

9.1 20.4 70.5   

Note. aCombined no and below average importance responses. bCombined above average and 
high importance responses. c Based on 5-point scale where 1 = no ability, 2 = below average 
ability, 3 = average ability, 4 = above average ability and 5 = high ability. 
 
The second objective was to determine teachers’ perceived ability to teach the PA topics and 
competencies (see Table 2). Using the vague quantifiers suggested by Lindner and Lindner 
(2024), teachers perceived their ability to teach each PA topic as below average. Teachers rated 
their ability to teach the soil sensing, unmanned aerial vehicle, and guidance and autosteering 

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topics highest, and the variable rate technology, real-time kinematic GPS, geographic 
information systems, and yield monitoring and mapping topics lowest. Between 61.4% and 
84.1% of teachers rated themselves as having no or below average ability to teach each of the 
29 PA competencies. Conversely, between 2.3% and 20.4% rated themselves as having above 
average or high ability to teach each of the 29 PA competencies.  

Table 2 
 
Arkansas SBAE Teachers’ Perceptions of Their Ability to Teach PA Topics and Competencies 
 Ability   

 
PA Topic 
Competency 

None or 
below 

averagea 
(%) 

 
 

Average 
(%) 

Above 
average 
or highb 

(%) 

 
 
 

Mc 

 
 
 

SD 
Soil sensing (SS)    2.29 0.69 
Select the correct sensor for a specific task 79.6 13.6 6.8   
Determine when to use grid vs. zone 

sampling 
84.1 9.1 6.8   

Determine when to use proximal and 
remote sensing 

84.1 11.4 4.6   

Use a computer program to analyze soil 
sensor data 

77.3 18.2 4.5   

Unmanned aerial vehicles (UAVs)    1.99 1.08 
Use computer program to analyze digital 

UAV images 
79.6 9.1 11.4   

Develop a “mission plan” to fly UAV 68.2 15.9 15.9   
Manually fly a UAV 61.4 18.2 20.4   
Select correct camera for specific UAV 

application 
75.0 11.4 13.6   

Guidance and autosteering systems (GAS)    1.93 1.21 
Operate a tractor in the field with lightbar 68.2 15.9 15.9   
Operate a tractor in the field with 

autosteering 
70.5 15.9 13.6   

Set an AB line and swath width for 
autosteering 

72.7 18.2 9.1   

Set an AB line and swath width for lightbar 72.7 18.2 9.1   
Variable rate technology (VRT)    1.76 0.88 
Identify the primary components of VRT 

system 
77.3 18.2 4.5   

Operate VRT equipment in the field 77.3 13.6 9.1   
Identify common sensors used in VRT 79.6 15.9 4.5   
Configure a variable rate controller 79.6 18.2 2.3   
Develop a prescription application map 77.3 20.4 2.3   

 

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 Ability   

 
PA Topic 
Competency 

None or 
below 

averagea 
(%) 

 
 

Average 
(%) 

Above 
average 
or highb 

(%) 

 
 
 

Mc 

 
 
 

SD 
Realtime kinematic GPS (RTK-GPS)    1.71 0.81 
Pair tractor or implement receiver (rover) 

with RTK base station 
84.1 13.6 2.3   

Explain basic operating principles of RTK-
GPS 

68.2 29.5 2.3   

Identify sources of position error in RTK-GPS 84.1 13.6 2.3   
Geographic information systems (GIS)    1.70 0.94 
Create a field map 72.7 20.4 6.8   
Determine sources of geographic data 75.0 18.2 6.8   
Edit a field map 75.0 18.2 6.8   
Describe coordinate system used in GIS 79.6 15.9 4.5   
Import data into a GIS program 79.6 13.6 6.8   
Yield monitoring and mapping (YMM)    1.68 0.95 
Identify common sensors used in YMM 

systems 
79.6 15.9 4.5   

Configure a yield monitoring system 79.6 15.9 4.5   
Calibrate a grain moisture sensor 79.6 13.6 6.8   
Identify primary components of a YMM 

system 
77.3 13.6 9.1   

Note. aCombined no and below average ability responses. bCombined above average and high 
ability responses. cBased on 5-point scale where 1 = no ability, 2 = below average ability, 3 = 
average ability, 4 = above average ability and 5 = high ability. 
 
The third objective was to determine the PA inservice needs of Arkansas SBAE teachers. When 
inservice needs were prioritized using MWDSs (see Figure 1), the highest need was in the 
geographic information systems topic (MWDS = 11.81) and the lowest inservice need was in the 
guidance and autosteering topic MWDS = 8.16). However, the relative magnitude of the 
MWDSs indicated inservice needs in each of the seven PA topics. By comparison, the lowest 
MWDS in this study was higher than the largest score (MWDS = 5.59) reported by Wells and 
Hainline (2021) in a national study (using the same 1 to 5 scaling) of teacher inservice needs 
related to more traditional agricultural mechanics competencies.  

 

 

 

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Figure 1 
 
Arkansas SBAE Teachers’ PA Inservice Needs as Prioritized by Mean Weighted Discrepancy 
Scores (Borich, 1980) 

 
 
The fourth objective was to determine the relationship between selected teacher 
characteristics and perceived PA inservice needs. The demographic variables years of teaching 
experience, experience with row crop farming, and PA experience all had negative correlations 
with PA inservice needs (as indicated by MWDS) for each PA technology (see Table 3). More 
experienced teachers, those with row crop experience, and those with precision agriculture 
experience indicated somewhat less need for inservice in each of the PA topics. The magnitude 
of these correlations ranged from negligible to moderate (Davis, 1971), with the largest 
correlation (r = -.36 between PA experience and the MWDS for unmanned aerial vehicles) 
explaining only 13.0% of the variance in inservice needs. 

 
 
 
 
 
 

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Table 3 
 
Relationship Between Selected Arkansas SBAE Teacher Characteristics and Inservice Needs by 
PA Topic 
 
PA Topic 

Years 
taught 

Row crop 
experiencea 

PA 
experiencea 

Geographic information systems (GIS) -.19 -.13 -.16 
Unmanned aerial vehicles (UAVs) -.19 -.26 -.36 
Realtime kinematic GPS (RTK-GPS) -.11 -.24 -.23 
Variable rate technology (VRT) -.23 -.19 -.02 
Yield mapping and monitoring (YMM) -.17 -.19 -.01 
Soil sensing (SS) -.25 -.11 -.17 
Guidance and autosteering systems (GASS) -.10 -.21 -.33 

Note. aCoded as No = 0 and Yes = 1 and analyzed using point-biserial correlations. 
 
The final objective was to describe teachers’ perceptions of the barriers to incorporating PA 
into their programs (see Table 4). Lack of equipment for teaching PA was perceived as a serious 
barrier for almost three-fourths (72.7%) of the teachers and as a moderate barrier by an 
additional 13.6% of teachers. A majority of teachers also indicated that lack of curriculum 
materials (84.1%), level of personal knowledge (81.9%), and lack of inservice opportunities 
(63.7%) were either moderate or serious barriers (combined categories) to incorporating PA 
into their programs. Conversely, only a minority of teachers perceived lack of administrator 
support (34.1%), lack of fit with state curriculum standards (27.3%), or lack of student interest 
(22.8%) as being moderate or serious barriers to incorporating PA into their programs.  

Table 4 
 
Arkansas SBAE Teachers’ Perceived Barriers to Incorporating PA into SBAE Programs 
 Not 

a barrier 
 Minor 

barrier 
 Moderate 

barrier 
 Serious 

barrier 
Potential barrier f %  f %  f %  f % 
Lack of equipment 1 2.7  5 11.4  6 13.6  32 72.7 
Lack of curriculum materials 0 0.0  7 15.9  18 40.9  19 43.2 
Level of personal knowledge 1 2.7  7 15.9  20 45.5  16 36.4 
Lack of inservice opportunities 1 2.3  15 34.1  20 45.5  8 18.2 
Lack of administrator support 12 27.3  17 38.6  10 22.7  5 11.4 
Lack of student interest 16 36.4  18 40.9  8 18.2  2 4.6 
PA does not fit state standards 21 47.7  11 25.0  10 22.7  2 4.6 

 
Conclusions, Discussion, and Recommendations  

 
This study used a non-probability sample of Arkansas SBAE teachers, so caution should be used 
in generalizing the findings beyond the respondents. However, because the study participants 

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self-selected to attend an inservice workshop on PA technologies they may be the most likely to 
teach PA in their agriculture programs. Additionally, the findings and recommendations may 
have practical implications for similar groups of teachers. 

Few Arkansas SBAE teachers had experience in row crop production or with PA. The MWDS for 
each PA topic indicated a high perceived need for inservice education with MWDSs higher than 
those reported by Wells and Hainline (2022). Therefore, teacher inservice workshops should be 
offered with immediate priority placed on geographic information systems, soil sensing, 
unmanned aerial vehicles, real-time kinematic GPS and variable rate technology. These 
workshops should focus on the specific PA competencies included in the survey instrument. 
Years of teaching experience, row crop experience, and PA experience were all negatively 
correlated to PA inservice needs but did not explain large amounts of variance in inservice 
needs. Therefore, little need exists to target PA workshops to specific subgroups of teachers. 

In addition to a lack of personal PA knowledge, teachers perceived that a lack of curriculum 
materials and a lack of equipment posed moderate to serious barriers to teaching PA. Because 
PA is not being taught in Arkansas SBAE programs, teachers are likely unaware of a variety of 
free and low-cost curriculum materials currently available from a number of sources. State 
agricultural education leaders should evaluate and curate a list of the best materials, 
categorized by PA topic and containing appropriate hyperlinks or contact information, and 
share this list with interested teachers.  

The cost of hands-on laboratory materials is a more difficult barrier to overcome. While PA 
educators have described relatively inexpensive models for teaching yield monitoring and 
mapping (Massey et al., 2020), variable rate application (Dickinson, et al., 2007; Massey & Kirk, 
2013), and geographic information systems (Nawaz & Sattar, 2016), some equipment is beyond 
typical SBAE program budgets. Agricultural educators should explore opportunities for 
equipment grants, cooperative ownership and sharing of teaching equipment, and industry and 
dealer partnerships to support hands-on PA instruction.  

Incorporating PA into the SBAE curriculum will be challenging. But, given the importance of PA 
and the employment and further education opportunities associated with PA, efforts must be 
made to overcome these barriers and incorporate PA into the SBAE curriculum.  
 

Acknowledgments 
 
This work was supported, at least in part, by the USDA National Institute of Food and 
Agriculture, Hatch project 1024473 and the University of Arkansas Division of Agriculture 

This work was based on Akwah, H. O. (2024). Inservice needs of selected Arkansas agriculture 
teachers related to precision agriculture systems. [Doctoral dissertation, University of Arkansas. 
Scholarworks@UARK. https://scholarworks.uark.edu/etd/5373/ 

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Author Contributions: H. O. Akwah – conceptualization, investigation, formal analysis, writing-
original draft; D. M. Johnson – conceptualization, investigation, formal analysis, writing – review 
and editing; G. W. Wardlow – conceptualization, investigation, writing – review and editing; C. 
Koparan – conceptualization, writing – review and editing; A. Poncet – conceptualization, 
writing – review and editing. 
 

References 
 
Association of Equipment Manufacturers. (2023, July 5). The environmental benefits of precision 

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