Geography and Sustainability 2 (2021) 312–327
Contents lists available at ScienceDirect
Geography and Sustainability
journal homepage: www.elsevier.com/locate/geosus
Towards smart farming solutions in the U.S. and South Korea: A
comparison of the current status
Susan A. O’Shaughnessy a,∗, Minyoung Kim b, Sangbong Lee c, Youngjin Kim c, Heetae Kim c,
John Shekailo d
a
USDA-ARS, Conservation and Production Research Laboratory, 2300 Experiment Station RD, Bushland, Texas USA 79012
R&D Coordination Division, Rural Development Administration, 54875, Republic of Korea
c
National Institute of Agricultural Sciences, Rural Development Administration, 54875, Republic of Korea
d
USDA-ARS, Office of International Research Engagement and Cooperation, Beltsville, MD 20705
b
h i g h l i g h t s
g r a p h i c a l
a b s t r a c t
• The South Korean government has estab-
lished a holistic vision for smart farming
Smart farming solutions in the U.S. are
discrete and driven mainly by private industry
• Cultural and political differences shape
alternate approaches to smart farming
solutions
• Frameworks for smart farming solutions
could facilitate achievement of sustainable development goals
•
a r t i c l e
i n f o
Article history:
Received 23 September 2021
Received in revised form 10 December 2021
Accepted 10 December 2021
Available online 26 December 2021
Keywords:
Agri-food
Climate change
Information technology
Sustainable agriculture
a b s t r a c t
Smart farming solutions combine information, data software tools, and technology with the intent to improve
agricultural production. While smart farming concepts are well described in the literature, the potential societal
impacts of smart farming are less conspicuous. To demonstrate how smart farming solutions could influence
future agricultural production, agri-business and rural communities and their constituents, this article compares
smart farming approaches and reasons behind the pursuit of smart farming solutions by the U.S. and South Korea.
The article compares agricultural assets and productivity among the two countries as well as the technical and
societal challenges impacting agricultural production as a basis to understanding the motivations behind and
pathways for developing smart farming solutions. In doing so, the article compares some of the technological
and social advantages and disadvantages of smart farming, dependending on the choice and implementation of
smart farming solutions. The South Korean government has implemented a national policy to establish smart
farming communities; a concept that addresses the entire agri-food supply chain. In the U.S., a national plan to
develop smart farming technologies does not exist. However, discrete smart farming solutions driven mainly by
competition in the private sector have resulted in high-tech solutions that are advancing smart farming concepts.
The differences in approaches and reporting of successes and failures between the two countries could facilitate
the rate of evolution of successful smart farming solutions, and moreover, could provide pathways to facilitate
sustainable development goals in developing countries where smart farming activities are currently underway.
∗
Corresponding author: Dr Susan Ann O’Shaughnessy, USDA-ARS Conservation and Production Research Laboratory, Soil and Water Research Management Unit,
2300 Experiment Station RD, Bushland, Texas 79012, United States, Phone: 1-806-356-5770, Fax: 1-806-356-5750.
E-mail address: susan.oshaughnessy@usda.gov (S.A. O’Shaughnessy).
https://doi.org/10.1016/j.geosus.2021.12.002
2666-6839/© 2021 The Authors. Published by Elsevier B.V. and Beijing Normal University Press (Group) Co., LTD. on behalf of Beijing Normal University. This is
an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
S.A. O’Shaughnessy, M. Kim, S. Lee et al.
Geography and Sustainability 2 (2021) 312–327
1. Introduction
towards a smooth transition from traditional farming to smart farming
and must also be addressed to ensure successful transfer of farm-holders’
rights.
Existing reviews on smart farming tend to have either a singular focus on the advanced technologies or have a heavy slant towards the political economic aspects of smart farming. This review juxtaposes technological advantages and disadvantages of smart farming with social
benefits and social challenges by comparing the status of smart farming solutions between the U.S. and South Korea, 1) beginning with a
discussion of agricultural resources and production systems; 2) briefly
describing the challenges facing sustainable agricultural production; 3)
investigating the frameworks and reasonings for the smart farming solutions developed; and 4) identifying the potential positive and negative
impacts that could result from the implementation of smart farming solutions. A discussion of each of these four topics as they pertain to either the U.S. or South Korea provides insight as to reasoning for each
country’s approach to smart farming solutions, predicted benefits and
potential negative impacts that smart farming could have on the actors
involved in agricultural production.
The definition and status of smart farming, sometimes referred to as
digital farming (Eastwood et al., 2019), varies from country to country.
Smart farming solutions apply information and technologies to increase
the economic yield of crop and livestock production, and to optimize
farming inputs and processes that extend to the transportation, distribution, and retail phases of the food supply chain (Nukala et al., 2016;
Idoje et al., 2021). These technologies rely on Big Data Analytics and include cyber systems that afford monitoring, smart predictions, decision
support, automated control and future planning (Wolfert et al., 2017;
Doshi et al., 2019; Moysiadis et al., 2021). Although there are many definitions for smart farming, the main conceptual elements found in the literature are similar and include combining Big Data Analytics and information communication technologies (ICT) such as the Internet of Things
(IoT) (Tzounis et al., 2017), and Edge and Cloud computing with farm
equipment, GIS technology, robotics, satellite images, unmanned aerial
vehicles (UAVs) and algorithms to accomplish farming practices innovatively and efficiently (Boursianis et al., 2020, O’Grady et al., 2019). In
addition, smart farms are expected to optimize food production by improving the application of nutrients to the soil, reducing the use of pesticides and water consumption in irrigation (Navarro et al., 2020). Precision agriculture, precision irrigation and AgInformatic systems (El Bilali
and Allahyari, 2018) are prime examples of current technologies that
could integrate ICT for optimizing farm inputs (Wolfert et al., 2017).
Yet, the smart farming concept was meant to be more holistic and include frameworks for establishing optimal farm processes, networking
of on-farm systems (Munz et al., 2020), monitoring the distribution of
farm products and marketing food commodities (Nukala et al., 2016;
Goel et al., 2021). Finger et al. (2019) predicts that smart farming
solutions could narrow the productivity gap between developing and
industrial countries. Several articles discuss how smart farming practices could narrow the productivity gap between developing and industrial countries by increasing competition and raising the standard of
living
Though much of the focus of smart farming constructs is on the fusion
of analytical and mechanical innovations and the potential benefits for
agricultural production, smart farming will also drive changes in societal
structures, the economy, business models, and public policy (Lele and
Goswami, 2017) as it relates to agriculture. Lombardi et al. (2020) and
Klerkx et al. (2019) argue that social innovation initiatives brought
about by smart farming could provide opportunity to strengthen relationships among rural populations, improve social networking and engender a new sense of ‘responsible professionalism’, which may prevent
rural marginalization. On the other hand, innovative changes could have
negative socio-ethical implications, such as widespread technical unemployment due to automation, cultural changes in farming practices
from a “hands-on” approach to a data driven approach (Bronson, 2018).
Furthermore, farmers may experience an identity crisis, especially if
they do not provide input to data driven decision-making. Other misgivings expressed by Bronson (2018) are that research and investment
in smart farms are biased towards large-commodity crop farmers, and
do not address the needs of medium-sized and small-sized farm holders.
Smart farming solutions in the U.S. and Canada have created ‘lock-in’
technologies, for example a packaging of proprietary crop seeds, specialized fertilizer and pesticide combinations, sensor monitoring systems and software that contains hidden algorithms to manage the data
from the sensors and have been used to maximize crop production
(Carolan, 2020). Today, the product service system (PSS) is a common
business model in many industries and is closely linked to innovation
and sustainability of businesses (Annarelli et al., 2016). The PSS facilitates monopolistic opportunities for large agrochemical companies
(Bronson, 2018). Rotz et al. (2019) warns that historically, the consequences of advanced technologies cause deleterious effects such as
land consolidation and cost-price squeeze that adversely impact small
scale and marginalized farmers. Marketing and distribution are critical
2. Methods
The research method used in this study was a literature survey,
searching on Scopus and Science Direct databases using “Smart Farming” in the title and key words of published journals. Agricultural data
was also collected from FAOSTAT, USDA-NAS and USDA-FAS, news
articles, country reports, and books. The data was used to provide a
comparison of agricultural resources, challenges, and approaches to
smart farm solutions between the U.S. and South Korea to understand
each country’s reasoning for pursuing smart farming solutions. Because
there is a dichotomy in opinion regarding the positive impacts from
the technological advances of smart farming and the potential negative societal impacts, this article includes a description of the positive
and potential negative impacts from the two different approaches pursued by the U.S. and South Korea. Information is also provided from
the field experience and communication that the authors have in working with producers and agriculture industry members within their own
country.
3. Background information
3.1. Agricultural land use, farm sizes and major crops
In 2020, approximately 363 million ha, 37% of total land area in
the U.S., was under agricultural production with more than 2 million
open-field farms in operation (USDA-NASS, 2021). At least 34% of the
farmed area was cultivated with grain crops for animal feed, such as
corn and sorghum, while acreage in soybean and wheat were roughly
25% and 13% of the total cultivated area, respectively. Acreage for
orchards, vegetables and melons (including potatoes) represented less
than 3% of total acreage in production, but these crops contributed to
more than 24% of the value of the principal crops grown in the U.S.
(USDA-NASS, 2020a, 2021) (Fig. 1).
Spatial distribution of these major crops shows that grain crops are
grown mostly throughout the Midwest and in the Northern and Southern
Plains regions. Cotton and soybeans are grown mainly in the southern region, while specialty crops are more abundant in the coastal regions near
California and Florida (Fig. 2). The average U.S. farm size in 2020 was
180 ha (444 acres), and the trend continues towards larger-sized farms
(Paul and Nehring, 2005). Organic farming is important to mention as
it represents 5% of agricultural sales and annual sales have increased
by 31% between 2016 and 2019. Certified organic acres operated in
the U.S. in 2020 (including cropland, pastureland, and rangeland) totaled 2.23 million ha. Of this acreage, approximately 1.42 million ha
produced organic crop commodities (USDA-NASS, 2020b). The reported
area dedicated to food crops under greenhouse production was 1,321 ha
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Geography and Sustainability 2 (2021) 312–327
Fig. 1. Percent of acreage by main crop types in the
U.S. (USDA-NASS, 2020a).
Fig. 2. Spatial distribution of the major crops
produced by region in the U.S. (USDA NASS,
2021)
(USDA-NASS, 2019a). Most crop producing farms in the U.S. are family
owned (USDA-NASS, 2021), and many families are members of agricultural cooperatives, existing as independent private businesses to enable
better access to financing, supplies and markets.
In South Korea, approximately 22% of land is arable, while the remaining land is mountainous or urbanized. Agriculture in South Korea strives to combine cultural heritage, societal needs, while emphasizing adaptation to local conditions and maintaining rural livelihoods
(Park and Oh, 2017). The total area cultivated for agriculture in South
Korea in 2019 was 1.58 million ha, representing a decrease of 29% from
1975 (Statistics Korea, 2020a) mainly due to land development for industrial complexes and residential housing (Choi, 2006). While agricultural acreage overall is decreasing in South Korea, farm size in the past
45 years has been increasing from 0.94 ha to 1.57 ha (Statistics Korea, 2020a). Acreage for rice paddy fields has also experienced a downward trend in the past 45 years. However, rice continues to be the
dominant crop grown in South Korea. In 2020, 52% of the total agricultural area was planted with rice and the remaining 48% of agricultural acreage was diversified towards production of other grains,
vegetables, fruits, specialty crops, and flowers (Fig. 3), data is from
FAOSTAT (2019).
While the cultivated area in the open fields decreased, the cultivated
area in protected facilities (greenhouses) increased by 7.2% per year
since 1979, and the absolute acreage in 2016 was approximately 83,629
ha. Fifty percent of the greenhouse acreage is dedicated to vegetable and
fruit production, 27% is relegated to condiment and root vegetables,
10% is dedicated to leafy and stem vegetables, 9% is devoted to fruit
trees, and the remaining 4% is for flowering plants (USDA-FAS, 2018).
Dietary changes are driving the percent land use changes for rice and
specialty crops. The spatial distribution of the main crop types produced
within the major provinces are shown in Fig. 4 (Statistics Korea, 2020c;
Statistics Korea 2020d).
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Geography and Sustainability 2 (2021) 312–327
Fig. 3. Percent of acreage by main crop types in South
Korea (data from FAOSTAT, 2019).
Table 1
Summary of Land and Water Resources for Agriculture in South Korea and the U.S.
Description
South Korea
U.S.
Total land under agricultural production
Average farm size (ha)
Percent acreage dedicated to grain crops
Acreage dedicated to greenhouse production (ha)
Daily water use (billion m3 )
Percent water used for agriculture
22% (1.58 million)
1.57
65% (mostly rice)
83,629
0.1
41%
37% (363 million)
180
34% (mostly corn and soybean)
1,321
1.22
40%
3.2. Available water resources
4. Challenges to agricultural production
In the U.S., river systems, reservoirs and aquifers play an important role in supplying water for everyday life. Total water withdrawals
from surface and groundwater sources in the U.S. per day in 2015
were approximately 1.22 billion m3 . Roughly 70% of the freshwater
withdrawals are from surface-water sources making precipitation and
snowpack data essential for supply forecasting of surface-water sources
(Fleming et al., 2021). Major withdrawals in the west are predominately
for irrigation, while those in the east are for thermoelectric power. Daily
withdrawals for agriculture represented 39.7 % of total water use in
the U.S. in 2015, of which nearly 50% are from groundwater sources
(Dieter et al., 2018). Dam structures have been used to increase water
storage capacity and distribution for agricultural production and to decrease climate uncertainty (Hansen et al., 2014). Pressurized irrigation
systems, mostly center pivot sprinklers, dominate the method of application to irrigated acres across the U.S. (USDA-NASS, 2019a).
Total annual water resources in South Korea amount to approximately 132.3 billion m3 . Annual water use in 2014 was reported to
be 37.2 billion m3 (Lee, 2019). Water use among agricultural, industrial and household sectors were 40.9%, 6.2 % and 20.4 % of the total
annual water used. Since two-thirds of the topography in South Korea
is mountainous, most rivers drain into reservoirs built to store runoff
and supply water during the dry season (Lee et al., 2018). However, a
constant supply of quality water is difficult to manage as roughly 43%
of surface water is lost through evaporation and soil penetration, while
during the rainy season, runoff is lost in floods and estuaries (Kim et al.,
2007). Data summarizing natural resources of land and water are shown
in Table 1.
Common challenges to the U.S. and South Korean agricultural sectors include sustaining food security amid decreasing quality water supplies and compliance with policy requirements to reduce agriculture’s
environmental footprint.
4.1. Competing and limited water resources
Throughout the U.S. there is competition for water between sectors
and states. Governance of water is different in each of the fifty states.
Historically state laws address statutory guidance for water use and
quality, but governance policies, ownership type (private or public), and
levels of enforcement vary from state to state (Schattman et al., 2021).
In many states, groundwater management districts comprised a variety of interest groups and local farmers establish management plans for
conservation, recharge and preservation of groundwater resources for
municipal and agricultural water use. Limited quality water resources
due to the depletion of groundwater from the Ogallala Aquifer in the
Great Plains region in south of Nebraska, and drought conditions in
the western and south-central U.S. continue to threaten crop production and reduce natural stream flow and snowpack (Mehrnegar et al.,
2021; Scanlon et al., 2021).
In South Korea, rural regions are vulnerable to water deficits in irrigation districts due to seasonal variations in precipitation and water
quality issues (Lee et al., 2019). Estimation of agricultural water demand
is critical for long-term planning and management (Nam et al., 2017).
In recent years, available agricultural water resources were gradually
diminished due to water shortages caused by drought and heat waves
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Geography and Sustainability 2 (2021) 312–327
Fig. 4. Map showing spatial distribution and proportion of main crops produced in major provinces within
South Korea (graphs are made from data at Statistics
Korea, 2019, 2020 a,b,c,d).
(Kim et al., 2018). Climate variability also makes it difficult to estimate
supply and demand.
sistently project a high vulnerability of the western states to climate
conditions (Jones et al., 2021). Direct effects of climate change on crops
and livestock include an increase in: (1) annual average and seasonal air
temperatures, (2) growing season length, (3) number of hot days and hot
nights, (4) variable precipitation patterns, and (5) higher concentrations
of CO2 (Janowiak et al., 2016).. It is estimated that these effects on crop
production will continue to be spatially and temporally variable across
the continental U.S., especially across counties in the Midwest where
grain crops are the predominant crop type (Lee et al., 2020). It is generally accepted that in some regions, predicted yields will increase while
in other regions, yields will decline (Johnston et al., 2015; Burke and
Emerick, 2016). States in the northern part of the country are expected
to see an increase in precipitation along with an increase in air temperature and growing season length. Yu et al. (2021) projected that by 2050,
increasing air temperature due to climate change will lead to a yield decrease in corn and soybeans in the U.S. by at least 13% and 57%, respectively. This forecast assumes that climate-neutral bio-technical changes
will continue to increase corn and soybean yields at annual rates like
those in the past 45 years. Suttles et al. (2018), using SWAT simulations, projected that streamflow would increase causing flooding, while
4.2. Environmental issues
Short- and long-term environmental challenges can increase the risk
of food insecurity. Disasters stemming from climate change events, water shortages, water quality issues and the recent COVID-19 pandemic
demonstrated disproportionate vulnerability to citizens with limited geographical access to food (those living in food deserts) and rural populations in both the U.S. and South Korea (Kim et al., 2020; Lewis et al.,
2021). Data from 2020 (FAOSTAT, 2020) , showed that approximately
2.6 million South Koreans, representing 5.1% of the population, were
moderately or severely food insecure, while in the U.S., approximately
26.5 million or 8% of the population suffered from food insecurity.
4.2.1. Climate change
Climate variability and climate change have altered the distribution
of water storage and water fluxes in the U.S.. Hydrologic vulnerability maps show that temperature and potential evapotranspiration con316
S.A. O’Shaughnessy, M. Kim, S. Lee et al.
Geography and Sustainability 2 (2021) 312–327
baseflow will decrease leading to extremely low flows in all future scenarios of land use and climate change in the southeast U.S. Changes in
climate and groundwater storage will affect future irrigated areas and
likely affect public policy (Evett et al., 2020).
The Korean peninsula is also highly impacted by climate change. For
the past century, the average ambient temperature in South Korea has
risen by 1.1 °C (Korea Meteorological Administration, 2020), and precipitation has increased by almost 160 mm annually (Jung et al., 2011).
Furthermore, there is a growing trend of longer summer and shorter
winter seasons (Government Republic of Korea, 2020). Currently, South
Korea experiences a 4 to 6-year cycle of extreme droughts and rainfall
events that result in extreme heat waves (Oh et al., 2017) and flooding under the East Asian monsoonal circulation (Kim et al., 2021a). The
country’s exposure to extreme conditions including total annual precipitation, daily maximum rainfall, drought duration and drought severity is
projected to continue to be spatially variable and occurrences are likely
to increase if greenhouse gases (GHGs) continue to be released at their
current rate (Sung et al., 2018; Choi et al, 2019; Van Doi and Kim, 2020).
The agricultural sector contributes nearly 3.4% to the total GHG emissions in South Korea, of which 58% is from crop cultivation and 42%
is attributable to livestock farming (Government of Korea, 2020). Using long-term spatial and temporal data, Nam et al. (2015) showed that
significant differences in annual reference evapotranspiration have occurred in the Midwest and Southwest regions of the peninsula since the
early 1970’s. Considering the current status of temperature, precipitation and extreme climate events in South Korea, a long-term outlook
suggests marked differences in the South Korean agricultural geography
after 2050 (Korea Meteorological Administration, 2020). Unexpected
environmental variables increase year by year and continue to threaten
food security in South Korea. The Scientific and Technological Prediction Survey (2012∼2035) suggests that water and food shortages are
linked to the intensifying trend of climate warming, and that the current situation of abnormal climates are megatrends, because they are
ultimately related to agricultural production.
decreased had an immediate and severe impact on U.S. farmers (USDAERS, 2021) and resulted in lower crop and livestock yields and a disturbance in the food supply chain (Haqiai and Horeh, 2021). In South
Korea, sales for in-person walk-in food markets dropped by 19.6%, and
online sales increased by 46 % (USDA-FAS, 2021). In both countries,
food and horticulture exports were down due to global cancellation of
events. Food service providers, food catering companies and farmers
were severely impacted from school and restaurant closures. The overall projection by the Organisation for Economic Co-operation and Development (OECD) is that the impact of the COVID-19 pandemic will have
ongoing effects throughout the next decade caused by a decline in consumer demand, and disruption in agro-food trading and the downstream
food processing industry (OECD, 2020).
4.3. Limited human resources and rural populations
The demographics of farmers in both countries indicate an aging
workforce and a shrinking rural population. The average age of U.S. producers in 2017 was 59.4 years (USDA-NASS, 2019b), with only 9.4% of
producers being 35 years old or less. Prior to the COVID-19 pandemic,
unemployment in non-metro areas had begun to decline and there was
a slight increase in rural populations. The upturn was due in part to better labor market conditions and recovering real estate markets in rural
areas (USDA-ERS, 2020). Nonetheless, more than 82% of the nation’s
population continues to be concentrated in big cities.
South Korea has a similar situation with most producers being 65
years or older (Statistics Korea, 2020b). South Korea is also experiencing a population decline in rural areas. The rural population in 2018
was 18.54 % of the total population (FAOSTAT, 2018), which represents a decrease of 84.4% as compared with its rural population in
1970 (Statistics Korea, 2020e). The aging and decrease in population
are due in part to urbanization and most younger citizens leaving for
cities where the living standards are higher ,and agricultural mechanization (Choi, 2006; Lee et al., 2021). According to Yoon et al. (2020),
in addition to the problems of an aging farmer population and reduction
in farmland, the free trade agreements with the European Union, China,
and the U.S. have weakened the competitiveness of domestic agriculture. A summary of challenges to agricultural production are listed in
Table 2.
4.2.2. Water Qquality
Managing water quality in river and ground water ecosystems is another shared challenge for sustainable agriculture in both the U.S. and
South Korea. Water quality is intrinsically tied to water storage levels,
stream flow and climate change. When estimating future life-cycle eutrophication, Lee et al. (2020) found that eutrophication in the Midwest
U.S. stays relatively steady when using the Representative Concentration Pathways (RCPs), developed by the Intergovernmental Panel on
Climate Change (IPCC), except in the scenario where GHG emissions
are high. High levels of GHG emissions from corn production in the
Midwest coupled with ambient temperatures and precipitation suggest
a sharp increase in eutrophication in the region by 2022 for a four-year
period and then again in 2057.
In South Korea, recent economic activity and the influx of pollutants have increased, therefore, as preventive methods, standard fertility
prescription, non-point pollutant control, organic farming with low energy, and livestock manure cycling have been implemented (Yoon et al.,
2020). The Rural Community Corporation, which supplies the right
amount of high-quality water required for farming in a timely manner by
managing agricultural reservoirs, pumping stations, and water canals,
has been monitoring water quality in real-time through automatic water
quality measurement devices, predicting water quality changes through
big data and artificial intelligence analysis and conducting preventive
water quality management.
5. Smart farming
In brief, the impetus behind smart farming solutions is to decrease
inputs without affecting yield quantity and quality. The strategies could
lead to increased profits, decreased environmental impacts, improved
land use productivity, and a shift in higher paying wages for agricultural
workers.
5.1. Definitions and elements of smart farm systems
The U.S. and South Korea are known for their innovative technologies (Branstetter and Kwon, 2018; Demircioglu et al., 2019), which
carry over to the agricultural sector. Historically, both the U.S. and
South Korea were dominated by an agrarian culture, but now both have
mixed economies. American agriculture began to experience a significant change in the early 1900’s transforming from a labor-intensive
sector to highly efficient mechanized operations (Dimitri et al., 2005).
South Korea quickly transformed to a leading economy in a single generation (Lee, 2019), in part due to comprehensive five-year economic
plans developed by the government and investment in social overhead
capital in the technology sector (Cardinale, 2019). The high degree of innovation and embracement of advanced technologies, serves both countries well in their quest towards smart solutions. Currently, both the U.S.
and South Korea are working towards the development of smart farming
systems or elements of smart farming to adapt to and mitigate the chal-
4.2.3. COVID-19 Pandemic
In 2020, the corona virus significantly disrupted the supply and demand cycle for agricultural products and disrupted agricultural distribution systems in both countries. The decline in food demand by restaurants and hotels coupled with reduced demand for biofuels as travel
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Geography and Sustainability 2 (2021) 312–327
Table 2
Challenges to Agricultural Production and Consequences in South Korea and U.S.
Description
South Korea and the U.S.
Climate change
Expected increase in floods, droughts, increasing ambient air temperature that will decrease yields. Inability to control the
impact of GHG
Heighten competition for water => increased costs for water and food
Polluted waterways and sources of groundwater; increased resistance in pests and weeds; decline of biodiversity within
agro-ecosystems
Reduction in rural populations will lead to anemic rural economies and decaying infrastructure; inability to attract younger
people to work on farms
Disruption in demand, limited supply chain, distribution, economic disaster, unemployment, food insecurity
Limited water resources
Environmental degradation
Limited human resources and aging population
Pandemic
lenges posed by limited resources, climate change and environmental
impacts.
algorithms with proprietary platforms to limit their compatibility between manufacturers.
Non-profit groups also play an indirect role in driving the development of concepts and elements of smart farming in the U.S. Examples
include the Council for Agricultural Science and Technology (CAST), a
nonprofit organization, that provides information to policy makers, the
media, private industries and the public. The CAST group developed a
position statement on Climate Smart Agriculture that emphasizes the
role that agriculture can play in helping address climate change while
creating jobs and economic opportunities (Baltensperger et al., 2021).
In addition, Ag Gateway, a global non-profit organization is helping to
frame smart farming on a national scale in the U.S. Its mission is to
develop resources and relationships that drive digital connectivity in
global agriculture and related industries. In working with the American Society of Agricultural and Biological Engineers (ASABE), AG Gateway pushed for the development of data exchange standards for transaction and electronic data compatibility (ANSI/ASABE Standards, 2018a,
2018b). This initiative was meant to standardize language and improve
data exchange across multiple hardware and software platforms to enable interoperability among sensors and equipment used in precision
irrigation technologies. Use of the standard by manufacturers and industry members is voluntary. A summary of the main smart farm concepts
for the U.S. is listed in Table 4.
In South Korea, concepts for smart farming solutions are more holistic. The Korean national innovation system was implemented to develop
regional economies based on technological innovation (Chung, 2002).
The system emphasizes the role of government in leading collaborative research and development to promote technological capabilities (Bae and Lee, 2020) and is perpetrated in the agricultural sector
(Choi, 2006; Jivany and Murray, 2006; Yoon et al., 2020) with the
dominant purpose being rural economic development. In the arena of
smart farming, the Korean government aims to improve productivity
and quality by enhancing ICT utilization through education, consulting,
and follow-up management. The Korean government views smart farming as a system to help guarantee the generational sustainability of agriculture, it is determined to change the national agricultural structure
to meet the trends and demands of the times, such as digitization and
low-carbon conversion. The Korean government also envisions smart
agriculture as a mean to continue to regenerate rural areas as the core
idea of the Korean version of the New Deal. Smart farming, which combines ICT and robot science technology such as big data, artificial intelligence, and the Internet of Things (IoT), is spreading and disseminating
to respond to the devastation to the agricultural environment caused
by climate change and solve the agricultural problems. As part of these
efforts, the Ministry of Agriculture, Food and Rural Affairs (MAFRA)
has been promoting agriculture for the purpose of upgrading agriculture, responding to the aging of farmers and nurturing young farmers.
MAFRA has set an expansion target by 2022 and is promoting ICT convergence projects in agriculture (e.g., facility horticulture, fruit trees,
and livestock), development of Korean smart farm models, and R&D support projects (Ministry of Agriculture, Food and Rural Affairs (MAFRA)
2019) (Fig. 5). The goal was to enable 7,000 ha of farms and orchards,
and 5,750 barns to operate as smart farms and smart operations, respec-
5.2. Approaches by the U.S. and South Korea
The U.S. passes legislation every five years, commonly known as the
“Farm Bill”, to address national agricultural and food policy. The current farm law applies through 2023. Policies are carried out through
a variety of programs including nutrition, crop insurance, commodity
support and land conservation (Agricultural Improvement Act, 2018).
While the farm bill authorizes and pays for mandatory expenditures and
establishes limitations for discretionary programs, a national American
approach to develop a smart farming system does not exist. Rather, advances in agricultural technologies and information systems that constitute elements in smart farming systems have been or are being developed mainly by the private sector, although public non-profit companies, and university institutions have had a role in agricultural innovations. In more recent years, corporations that invest in agricultural R&D
are prone to mergers and acquisition (Chai et al., 2019). Smart farming
solutions designed in the U.S. are mostly hardware or software products (Pivoto et al., 2018) that can operate independently or in combination to provide farm management processes. Examples are GPS-guided
tractors, yield monitors, variable rate sprayers for pest control, planters
and variable rate fertilizer implements. All these technologies have been
widely adopted in the U.S., mainly because this equipment allows farmers to manage large-size farms more efficiently and optimize more precisely the inputs with no additional human labor (Bora et al., 2012;
Kolady, et al., 2021; Pandey et al., 2021). Currently, in the U.S., smart
system products developed by private industry (such as Bayer, CropX,
John Deere, Lindsay Corporation, Reinke and Valmont, Industries) are
available to farmers on the retail market. Universities and the Agricultural Research Service (ARS under the Department of Agriculture) are
also involved in developing smart farming solutions for precision irrigation management in collaboration with private industry or with state
cooperative extension specialists (Evans et al., 2011; Gorli and G, 2017;
Pandya et al., 2019; Andrade et al., 2020; Jiménez et al., 2020). Specific
smart system solutions include automation and equipment control (such
as pumps, tractor guidance), optimization of machine operations (e.g.,
tracking maintenance parameters), or provision of decision support tools
for irrigation scheduling, forecasting precipitation, or developing variable rate application maps for fertilizer or irrigation (Thomasson et al.,
2019). The market for smart hardware also addresses the need to reduce
the time that a grower spends monitoring and making agronomic decisions for large-size fields or for multiple fields. Decision support algorithms are data driven and typically based on any one or a combination
of in-situ sensors, image sensors, imagery from UAVs or satellite systems
in combination with edge or cloud computing and machine learning algorithms. Information is acquired by farmers using mobile phone apps
or web-based computer sites. In many cases irrigation companies are
working with software firms and tech companies that offer geoinformation services to provide a whole package solution. The shortfall of these
smart hardware and software solutions are that they often use unique
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Fig. 5. Smart farm concept as part of the “Smart Farm Dispersion Method” in South Korea (MAFRA, 2021).
Table 3
Generation classification of the Korean smart farm and its commercial outlook (MAFRA, 2019; Yoon et al., 2017)
Classification
1st generation
2nd generation
3rd generation
Commercialization time
Target effect
Current
Convenience
improvement, ’more
comfortable’
Remote facility control
Environment
Telecommunication
Human/Human
Year of 2030
Increased productivity, ‘less input, more production’
Year of 2040
Improve sustainability, ’everyone with high production with
better quality’
Precision growth management
Crop growth
Telecommunication, Big data/Artificial intelligence
Human/Computer
Life cycle intelligence, automatic management
Production
Telecommunication, Big data/Artificial intelligence, Robot
Computer/Robot
Main function
Key information
Core technology
Decision- making/control
Table 4
Main concepts behind smart farming solutions
Description
South Korea
U.S.
Vision
Main driver
Supplementary drivers
Model
Holistic smart communities; regenerate rural areas
Government collaborations
Private sector
Nationalistic, technology centric
Discrete hardware and software solutions
Competition among private sector
Non-profits, universities
None
ing and control, the 2nd generation-improved productivity through intelligent precision growth management, and the 3rd generation-export
of smart farm integrated system such as energy optimization and robot
automation of the technology are developed and put into practical use.
The project plans to reduce the use of labor and agricultural materials,
link it with farm household income through productivity and quality
improvement, and further solve the difficulties in the farming field and
related industries at the same time. Currently, because the ICT devices
being distributed are not compatible with each other due to the different product specifications of each company, the integrated management
and maintenance of smart farms is difficult. Accordingly, ICT equipment
standardization and other standardization work are underway to unify
the format and communication method into one common standard for
various sensors and controllers used in horticulture and livestock.
While South Korea emphasizes smart farming communities, the government also embraces discrete smart farming solutions in the form of
smart agriculture equipment blended with the idea of digital agriculture which combines ‘precision agriculture’ technology with intelligent
tively, by 2022. Since 2018, for the spread and advancement of smart
farms, the creation of a youth startup ecosystem, establishment of industrial infrastructure, and creation of a smart farm innovation valley
are being promoted as major policy tasks (Ministry of Strategy and Finance, 2018).
The Rural Development Administration (RDA) of the Republic of
Korea (the government institution that conducts agricultural research,
technology dissemination and international cooperation) has been concentrating its research capabilities on securing key elements and sourcebased technologies to develop the world’s best Korean-style smart farm
model, and to make the entire process of the perch production system
smart. The Korean smart farm project is a long-term project to secure
independent agricultural production technology that can compete with
advanced agricultural countries by developing technology suitable for
agricultural environment and field conditions without importing, applying, or simply imitating foreign advanced technology. This Korean
smart farm prototype follows a technology model with various levels
(Table 3): 1st generation-improved convenience with remote monitor319
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Geography and Sustainability 2 (2021) 312–327
network and data management and utilizes big data and artificial intelligence (AI) for decision support. Smart farm applications are currently
being used in greenhouse production (Hyunjin, 2020) and field production (Kim et al., 2021b).The main smart farming concepts for Sourth
Korea are summarized in Table 4.
maintenance of the weather stations. New methods to improve ETo estimates using AI algorithms using less instrumentation (Choi et al., 2018b;
Jeong et al., 2018) or downscaling weather data to produce daily highresolution meteorological data for model inputs where ground-based observations are not available (Gupta and Tarboton, 2016) could be used
by smart farming communities and by different smart farm solutions.
5.3. Other smart farming and ICT applications
Smart farming has the potential to reduce labor and increase efficiency of agricultural inputs and time management for producers, this
would benefit both countries. Reduced inputs with limited reduction in
quantity and quality of yield could translate into profitability. Smart
farming also has the potential to reduce the risk of crop loss and failure due to climate change. Sector growth is envisioned as long as the
ICT system affords data strategies providing intelligent information and
services to farmers such as potential buyers for their products and predictions for future demands (Sedek et al., 2021). The global market for
smart agricultural goods was estimated at 6.34 billion USD in 2017; this
market is projected to reach 13.50 billion USD by 2023 (O’Grady et al.,
2019).
5.3.2. Smart farming to address climate change and carbon sequestration
In order to stabilize the supply and demand of food, precision irrigation technology could help to accurately supply the amount of water
necessary to the crop when it is needed, and then increase water availability (Kim and Kim, 2019).
Adaptation strategies to manage climate change could also benefit
from smart farming solutions. The U.S. has restored its commitment to
address climate change at home and abroad (Exec. Order No. 14008,
2021). Policies and strategies to pursue green recovery efforts, initiatives to advance clean energy transition, sectoral decorbanization and
alignment of financial flows with the objectives of the Paris Agreement are under formulation. It is argued that carbon pricing and technology policy are necessary for effective climate change to take place
(Baranzini et al., 2017; Metcalf, 2019 in Dumortier and Elobeid, 2021).
The CAST group, introduced earlier, recommends various plant and soil
management practices to improve carbon sequestration and reduce carbon losses (Baltensperger et al., 2021). The Agricultural Research Services (ARS) is providing solutions to mitigate GHG, reduce the effect of
climate change on livestock and crop production and create adaptive
and resilient spillway systems through their Climate Hubs and Regional
Biomass Research Centers. Climate Smart Agriculture is an approach
that lends itself to the smart farming concept. Growers armed with realtime information and decision support from models could strategize on
crop choices to sustain net profitability when seasonal droughts or floods
are forecasted, and precision managed inputs with site-specific sensor
input and feedback (Kalaiarasi et al., 2018).
Driven by the impacts of climate change, South Korea developed a
strategic policy to reach carbon neutrality by 2050. Their vision is to
harness green innovations and advance digital technologies to create
synergies between their Green New Deal and Digital New Deal plans
(Lee and Woo, 2020). They realize that mitigating climate change requires cooperative engagement at the global scale, and they are ready
to lead by example. The idea of green innovation includes encouraging
farmers to adopt improved irrigation of rice paddies and use of lowmethane fodders to improve livestock enteric formation, the expansion
of clean power and hydrogen across all sectors, improving energy efficiency and reusing wastes, commercial deployment of carbon removal,
scaling up a circular economy for industrial sustainability and enhancing carbon sinks by restoring forests and improving their management
and by creating urban green spaces. A certification program for farmers has been developed to incentivize the application of minimal inputs
and consumers are asked to do their part by generating less food excess
and changing their dietary behaviors to reduce their daily carbon footprints. Table 5 summarizes some key smart farming applications that
could benefit agricultural productivity in the U.S. and South Korea.
5.3.1. Use of ICT for water management at different scales
While several applications for smart farming are listed in the
approaches by both countries, there are other applications tangential to agriculture that could benefit from implementation of ICT in
both countries. These applications include monitoring and control of
interstate river systems (USGS, 2018), water storage facilities, water conveyance systems, water quality at the watershed and farm
level and providing water supply forecasts (Fleming et al., 2021).
Asquith et al. (2020) demonstrated that when copious data collected
by multiple agencies exist, reliable month estimates of the water level
in the Mississippi River Valley alluvial aquifer are achievable using general additive models and support vector machines. This example shows
that sharing big data through ICT could be used to improve allocation
of a shared water resource.
In South Korea, Hong et al. (2016) established a web-based decision
support system to manage irrigation canals for crop production. Combining the system with ICT could provide real-time water-level monitoring based on an automated water gauge system to open and close gate
valves for canal systems across the country. By doing so, canal water
management policies could be linked on a regional scale, leading to improved canal water management policies enabling irrigation planners
to optimally manage scarce water resources. Jung et al. (2020) demonstrated that the Korean Land Data Assimilation System (KLDAS) could
monitor soil moisture at a high resolution to provide long term soil moisture estimates over the country. The system relies on land surface models and precipitation and soil texture maps at the resolution of 0.01°
gridded data. Including such as system in Smart Farming communities
could provide forecasts of drought conditions and enable preparation
for water supply policies, encourage management of water resources
including rain harvesting, water storage, and implementing irrigation
scheduling techniques. Like the U.S., South Korea is also experiencing
ground water contamination from nitrogen run-off in agro-livestock districts (Kim et al., 2019). Monitoring groundwater quality, informing
livestock farm managers and automating chemical mitigation could improve drinking water standards.
Another application that could benefit from the integration of ICT in
both countries is to develop robust agro-mesonet systems where weather
data can be accessed real-time and used to estimate reference evapotranspiration (ETo ). Using ETo to estimate crop water use (ETc ) for different cropping systems is a mature method to determine the timing of
irrigations and the amount of water to apply (Allen et al., 1998), and
could result in savings from reduced pumping costs (Marek et al., 2020).
However, providing daily ETo and specific ETc values to farmers has
become a challenge due to funding shortages for instrumentation and
5.4. Common challenges created by smart farming
Information technologies are the backbone of smarting farming applications (Castañeda-Miranda and Castaño-Menseses, 2020) and accessibility, reliable infrastructure for data transmission and collection, and
human resources and expertise to analyze data are critical to the adoption of ICTs (Kamilaris et al., 2017). There are also socio-economic concerns that surround smart agriculture as well as issues concerning data
governance and incompatibilities within the suite of technologies being
used.
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Table 5
Smart farming applications to improve agricultural productivity in South Korea and the U.S.
Description
Specific examples
Water management
Monitoring and control of interstate river systems, water storage facilities, water conveyance systems, water quality at the
watershed and farm level and forecasting water supply needs
Estimating evapotranspiration to combine with crop coefficients for irrigation scheduling; forecasting extreme weather events
Develop programs for carbon sequestration credits (local and global banking); reduce risk of total crop failure; predict future
crop demands; incentivize best management practices to minimize agricultural inputs
Agro-mesonet Systems
Adaptation strategies to manage climate change
5.4.1. Accessibility to internet or unreliable connectivity
Presently, access to high-speed internet in some areas of the U.S. is
limited, unreliable or unaffordable (Mark et al., 2016; O’Grady et al.,
2019; Strover et al., 2021) and access to very-high-capacity fiber-tothe-premises (FTTP) in the U.S. lags behind most high-income countries
including Korea (Rajabiun and Middleton, 2018). To date, high tech
advances in broad band communication technologies (5G and Wi-Fi 6)
have only been rolled out in these populous regions due its expense and
the high-density requirement for micro cells (Glass and Tardiff, 2021).
South Korea implemented national policies encouraging a certification procedure and construction of an advanced broadband internet infrastructure that is utilized by research institutes and universities, and jointly invested in by private carriers as well as the government. Regulatory policies place minimal regulation on the broadband
market (Lee and Chan-Olmsted, 2004). South Korea has increased its
bandwidth capacity and the number of secure servers installed in the
country. Approximately 99.5% of the residences have internet access
(Sant’Ana et al., 2021).
In both countries, farmers are predominantly vulnerable to lack of
internet due to their rural and often remote status. In general, the problem in the U.S. is twofold: lack of infrastructure which causes some to
have no internet access; and intermittent, poor-quality connections with
limited bandwidth and slow speed (Mark et al., 2016; Drewery et al.,
2019). Neither situation could sustain a smart farming system.
sideration of potential benefits of adoption. Finally, the cost to operate
smart farm systems could be a financial drain.
In the U.S., the adoption of smart farm elements such as GPS-guided
tractors and sensor systems that allow on-the-go adjustments of equipment such as variable rate fertilizer applicators or spray control for herbicide and pesticide application have been successful (Evett et al., 2020).
Similarly, ICT systems for the remote monitoring of moving irrigation
sprinklers experienced early widespread adoption (Kranz et al., 2012).
However, farmer adoption of sensors for scientific irrigation scheduling
and systems for variable rate irrigation are limited (O’Shaughnessy et al.,
2019; Marek et al., 2020; Taghvaeian et al., 2020). Reasons for the lack
of adoption of sensors advanced technologies in agriculture in the U.S.
are somewhat similar to those in South Korea. Farming around sensors
deployed in a field is often viewed as an inconvenience at best and a
disruption at worst to in-season farming practices. Understanding sensor network telemetry and maintaining and repairing sensor equipment
requires time and adds complexity to farm operations. Also, performing quality assurance on raw data, and computational difficulties in
extracting high-quality decision support from data in a real-time are
often viewed as drawbacks to sensor deployment and implementation
(Vermesan et al., 2011).
The process of data collection and data storage also opens questions regarding hardware and software compatibility and interoperability among sensors and software platforms, especially when multiple
manufacturers are involved. Often, telemetry communication systems
are proprietary and there is reluctance to use open-source code, this situation limits sensor choices. Furthermore, there continues to be a gap
in deriving robust decisions from acquired data even when third parties
are involved in providing the recommendations (McLaren et al., 2009;
Mark et al., 2016; Drewry et al., 2019).
5.4.2. Data privacy, security and ownership
The collection of big data and its analysis creates concerns of data
privacy, ownership and cyber-security (Wolfert et al., 2017; El Bilali and
Allahyari, 2018; Drewery et al., 2019). A prime example is UAS imagery,
which is projected to be a key benefit to U.S. agriculture and smart farming solutions in many locations. However, the smooth integration of UAS
information into agriculture has lagged in the U.S. because of prevailing
social issues including concerns related to safety and privacy invasion
(Freeman and Freeland, 2014). A study by Chae et al. (2016) proposed a
method to distribute, store and decrypt user authentication information
in smart farms to prevent security vulnerabilities.
5.4.4. Other barriers to smart farming
Wolfert et al. (2017) emphasizes that smart farming requires collaboration between many different stakeholders having different roles in
the data value chain. This requires additional administration, standardization and mutual adjustments for growers as they determine how best
to work with different businesses such as venture capital and tech companies. In Klerkx et al. (2019), digitalization of farming often demands a
different skill set of farmers that can alter traditional ‘hands-on’ farming
practices to data driven decision making. Therefore, traditional farmers
may view smart farming as a threat to their livelihood.
Ehlers et al. (2021) discusses the impact of more data in a different light, listing a range of implications for agricultural policies that
could form within the digital farm agro-ecosystem, for example, a transition from direct intervention to information-based governance. In time,
policies may shift from data monitoring towards regulation that favors
outcome-oriented policies to collect taxes, and control inputs harmful
to the environment. Also, economic incentives could accrue, allowing
farms that demonstrate efficient input-outcome correlations more flexibility in allocating their resources, or subsidies could be awarded to
farms sharing their data. Smart farming solutions, especially that of
holistic smart farming communities face resistance from farmers because of unfamiliarity with technological changes, disbelief that smart
farming can be used effectively on small farms, alteration of the current
relationship between farmers, their farms and their customers. Furthermore, the impact of upfront cost to upgrade the ICT infrastructure and
5.4.3. Resistance to technology adoption
Adoption of technology by farmers in both countries has historically
been a challenge. Technology generally adds upfront costs and complexity, and requires a learning curve to operate and understand new hardware and software systems. Hardware devices, software, training and
other expenses in addition to conventional irrigation costs are likely to
be a burden for small-sized farms (Walter et al., 2017). Often, there is
no assurance that the upfront monetary and time investments will result in improved outcomes realized in increased yields or profitability
(O’Grady et al., 2019). Yoon et al. (2020) reported that the use and
awareness of ICT technology by farmers in South Korea remains relatively low. They provide specific insights as to possibilities of the lack
of smart farm adoption. These include non-commensurate levels of compatibility between the technology needed to convert to smart farming
and a farm’s readiness to embrace technology. This reasoning is similar to that of European farmers who are non-adopters of smart farming
technology, as they believe the appropriate technology is not available
or accessible (Kernecker et al., 2020). Secondly, the digital environment
can be seen as a threat to a farm’s corporate culture, overshadowing con321
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Table 6
Common technical and social challenges confronting the development and adoption of smart farming solutions.
Description
Issues
Data and cyber management
Technology adoption
Concerns over information ownership and privacy; security vulnerabilities
Behavioral impact are the lack of trust that technology will be available, accessible, and affordable; and economic aspects:
high upfront costs
Traditional farmers trust decision making based on ‘hands-on’ experiences or visual observations; not comfortable with data
derived decision-making
Unfamiliar or unable to fully realize the changes needed for a successful business model that links ICT and businesses
associated with the entire food supply chain
Extreme change in agricultural
practices
Inadequate business models and
business partnerships
Fig. 6. Differences and similarities driving
smart farming solutions in the U.S. and South
Korea.
fields (< 0.001% are produced in protected environments), most farms
are privately owned and managed by families, and farm size in the U.S.
is significantly larger than in South Korea. In South Korea, water resources are managed by the Ministry of Environment (Lee, 2019), 5.3 %
of crops are produced in greenhouse environments, and most farms are
small scale. These differences play a role in shaping each country’s diverse approach to smart farming solutions (Fig. 7). For example, due to
economy of scale, larger-sized farms can more easily support the fixed
cost of smart farming solutions and can reduce labor costs (Basso and
Antle, 2020); government incentives may not be necessary for farmer
adoption. However, smaller-sized farms likely need subsidies to support
the upfront costs of high-tech innovations. Pressure to achieve national
food security could be heightened if productive land capacity becomes
limited (Fitton et al., 2019). The arable land per capita for South Korea
is 0.31 ha, conspicuously low as compared with a value of 1.11 ha per
capita in the U.S. According to Kumar et al. (2012) and He et al. (2019),
such low values could signal vulnerability to local food shortages. It is
speculated that this threat could be a driver for South Korea to succeed
in implementing smart farming communities. Irrespective, South Korea
is following a smart farming approach in which the farmer is part of a
highly integrated food supply chain, while the approach by the U.S. is
market driven in which a farmer selects a discrete solution. These varied
approaches are prophetically described by Wolfert et al. (2017). Each
approach has its own advantages and disadvantages. South Korea’s nationalistic plan embraces a holistic concept that addresses not only optimizing farm processes but seeks to optimize networking for on-farm
systems, enhances monitoring of farm product distributions, and facili-
monitoring systems are a deterrence (Lioutas and Charatsari, 2020). Except for access to reliable internet services, both the U.S. and South
Korea share similar challenges to developing useful smart farming solutions and similar barriers of farmer adoption. These are summarized in
Table 6.
6. Discussion
In this review, information from the U.S. and South Korea on agricultural resources, challenges for sustainable crop production, frameworks
for smart farming solutions and potential positive and negative technological and social aspects were discussed. Both the U.S. and South Korea
have similar drivers in the form of objectives and challenges to invest in
innovative smart farming solutions. However, their approaches to these
solutions are different and mainly shaped by farm size, cultural farming
practices, technology readiness, and government policies. The shared
goals are to help ensure food security, improve sustainable agricultural
practices and provide economic sustainability for rural economies. The
shared challenges are limited water resources for agricultural use, competition for quality water from other sectors, adverse impacts from climate change, degraded water quality, complex water management issues, environmental degradation and an aging workforce (Fig. 6).
Both countries produce a variety of crops, and the spatial distribution
of crop production indicates the economic importance of agriculture to
each state and province in the U.S. and South Korea, respectively. Significant differences between the countries are linked with varying water
policies from state to state within the U.S., most crops are grown in open
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Fig. 7. Unique approaches, shared concepts and common challenges among the U.S. and South Korea relative to smart farming solutions.
However, smart farming solutions come with disadvantages. The
new technologies will introduce various facets of complexity to agricultural production and business practices. More so, it is well known
that successful agricultural innovative transformations are not simply
about technology advancement and adoption, but also require institutional change in data ownership, markets, labor forces, and land tenure
(Klerkx et al., 2010). In the U.S., formulating smart farming solutions
that are profitable may initially be challenging, because of the lack of
existing agro-business models to opimize profitably of the entire food
supply chain and because of the effort required to overcome incompatibility between software and hardware products, and cyber provisions
and security issues. The South Korean smart farming vision is ambitious
and seeks to revolutionize rural communities, but the goals may take
longer to accomplish because of the inherent complexity of technological and sociological changes required to transform whole communities
into smart solutions.
Neither country has yet established policies for data governance or
fair business models. This is critical because smart farming involves
partnerships with various companies that have not traditionally been
involved in agriculture. If holistic smart farming models are to be implemented, diverse companies must work together and develop business
models encompassing the entire system and support each business member’s role. Without meaningful data policies in place, and tolerance for
the interdependence of each company’s role, adoption rates may continue to lag. However, competitive pressure to rebuild the strength of
domestic agriculture has driven government support for smart farming
solutions in the form of policies and finances; these incentives could
encourage adoption by South Korean farmers (Yoon et al., 2020). In
the U.S., as farms become larger, and financial risks for success become more difficult, smart farming solutions leading to efficiency, convenience and increased profits could be primary drivers for adoption.
Government and citizens in both the U.S. and South Korea have a
vested interest to pursue sustainable agriculture as one facet of sustaining national security and natural resources. The path for each country is
filled with conundrums, yet the sharing of ideas, successes, and failures,
as well as engagement in collaborative scientific research could result
in advancements towards smart farming solutions and in the long-term,
a systematic approach towards sustainable agriculture. For both countries to achieve success, the solutions must be profitable for farmers,
ICT firms, the sensor industry, and all members of the smart farming
solution chain. Software, hardware and smart farming systems must be
tates the marketing of domestic food commodities and rural economies
as tourism enterprises (Hawng and Lee, 2015). The Rural Development
Administration announced the ’Basic Plan for Promotion of Digital Agriculture’ (hereinafter referred to as the Basic Plan for Digital Agriculture)
to realize scientific farming and sustainable agriculture based on Big
data. The Digital Agriculture Basic Plan, which has been promoted as
a five-year plan from 2021 to 2025, consists of 10 tasks in three areas.
The three areas include: 1) establishment of agricultural technology data
ecosystem, 2) digital innovation of agricultural production technology,
and 3) digital agricultural technology that supports distribution, consumption and policy (Yeanjung, 2018).
Conversely, the U.S. does not have a national plan to implement
smart farming communities. However, discrete smart farming solutions
driven by competition within the private sector are thriving. Farmers
in the U.S. have adopted smart hardware solutions associated with GPS
and variable rate technologies, and some software solutions that provide
decision support for site-specific fertilizer and irrigation scheduling applications. Smart phones, irrigation scheduling apps, and cellular and
WiFi communication technologies are used extensively in North America to monitor and control the operation of irrigation sprinklers and
pumping systems. Decision support products based on artificial intelligence are also beginning to emerge on the market. As U.S. farmers
continue to experience profitability in precision agricultural technology
(Schimmelpfennig, 2016) and as farm sizes increase, the future market
for smart farm technology in the U.S. will likely remain strong. Further, the participation from universities, government funded agencies
and technical companies involved in research and development of information and communication technologies, and hardware and software
platforms are facilitating the adoption of smart farm solutions.
Key components to move agriculture towards smart farming solutions in any country are innovation, mobile technology, broadband access, access to quality water, nutrients and knowledge (Goel et al.,
2021). Even though the U.S. and South Korea have yet to establish
widely adopted smart farming solutions or holistic smart farming systems, both countries have the technical readiness to establish the necessary links between innovative hardware and ICT. Low-hanging fruit for
both countries could be to establish smart farming solutions that control greenhouse and livestock production, orchard production systems
and automate water conveyance and irrigation scheduling management
(Gómez et al., 2019; Gorli and G, 2017; Kim and Kim, 2019; Iddio et al.,
2020).
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Fig. 8. Graphic summarizing the investment needed to develop smart farming solutions, as well as the major consequences and hurdles that will likely follow before
obtaining the desired intermediate and final goals.
affordable, accessible and user-friendly. All members of smart farming
solutions must experience non-tangible and tangible benefits resulting
from sustainable agriculture. While the U.S. agricultural economy is not
likely to embrace the South Korean smart farming community concept,
South Korea could find it advantageous to adopt U.S. smart farming solutions and apply them to South Korean smart farming communities.
Other countries around the globe are facing the same challenges to
sustainable agricultural production as the U.S. and South Korea (Li et al.,
2019). FAO has long supported sustainable development for people, the
planet and prosperity. Food and agriculture are critical to achieving the
entire set of sustainable development goals (SDGs) and smart farming
solutions could be customized to aid developing countries in achieving SDGs. Smart farming solutions, especially those focused on climatesmart agriculture are being established in developing countries, however, many of the same challenges faced by the U.S. and South Korea
are arising. Branca and Perelli (2020) report that African agriculture
systems need to be altered to expand crop production capacity and minimize environmental impact. Limitations to smart farming in Africa include access to financial resources, scaling up technological innovation,
and the lack of farm to market links within the food supply chain. Drivers
needed for widespread technology adoption include database expansion where information is sparse, farmer education, and national policies to improve socio-economic conditions, location specific agricultural
technologies (such as crop diversification, crop rotation, water management), policy shifts to promote smallholder farmers, and formulation of
a business model to establish and sustain a successful agri-food production chain (Habtewold, 2021; Ng’ang’a et al., 2021; Sedebo et al., 2021;
Zerssa et al., 2021). The need for practical application of appropriate
smart farming technologies provides opportunity for the U.S. and South
Korea to transfer technical and socio-political frameworks towards the
development of appropriate smart farming solutions to aid developing
and industrial countries with their intermediate goals and end goal to
achieve sustainable agriculture (Fig. 8).
for smart farming solutions are similar and based on similar challenges
to natural resources, adverse impacts from climate change, and aging
workforce and environmental issues. Both countries are pursuing smart
farming solutions as a strategy towards improving sustainable agricultural practices. However, their approaches vary, and the dissimilarities
are mainly shaped by differences in farm size, cultural farming practices, technological readiness, and government policies. The different
approaches to smart farming solutions and the reporting of successes
and failures from the two countries could provide new solutions to facilitate the evolution of smart farming and SDGs in developing countries
where smart farming activities are currently underway.1
Declarations of Competing Interest
The authors declare that there is no known competing financial interests or personal relationships that could have appeared to influence
the work reported in this paper.
Declaration of interests
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
1
The U.S. Department of Agriculture (USDA) prohibits discrimination in all
its programs and activities on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal,
or because all or part of an individual’s income is derived from any public assistance program. (Not all prohibited bases apply to all programs.) Persons with
disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET
Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, Director, Office of Civil Rights, 1400 Independence Avenue,
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mention of trade names of commercial products in this article is solely for the
purpose of providing specific information and does not imply recommendation
or endorsement by the U.S. Department of Agriculture.
7. Conclusions
This paper describes the similarities and differences in resources
available for agricultural production and the challenges impacting sustainable agricultural practices in the U.S. and South Korea. The drivers
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Geography and Sustainability 2 (2021) 312–327
Acknowledgements
Doshi, J., Patel, T., Bharti, S., 2019. Smart framing using IoT a solution for optimally
monitoring farming conditions. Proced. Comput. Sci. 160, 746–751.
Drewery, J.L., Shutske, J.M., Trechter, D., Luck, B.D., Pitman, L., 2019. Assessment of
digital technology adoption and access barriers among crop, dairy and livestock producers in Wisconsin. Computers Electron. Agric. 165, 104960.
Dumortier, J., Elobeid, A., 2021. Effects of a carbon tax in the United States on agricultural
markets and carbon emissions from land-sue change. Land Use Policy 103, 1105320.
Eastwood, C., Klerkx, L., Ayre, M., Rue, B.D., 2019. Managing socio-ethical challenges in
the development of smart farming: From a fragmented to a comprehensive approach
for responsible research and innovation. J. Agric. Environ. Ethics 32, 741–768.
Ehlers, M., Huber, R., Finger, R., 2021. Agricultural policy in the era of digitalisation.
Food Policy 100, 102019.
El Bilali, H., Allahyari, M.S., 2018. Transition towards sustainability in agriculture and
food systems: Role of information and communication technologies. Inform. Process.
Agric. 5, 456–464.
Evans, R.G., Iversen, W.M., Kim, Y., 2011. Integrated decision support, sensor networks
and adaptive control for wireless site-specific sprinkler irrigation. Appl. Engr. Agric.
27 (3), 377–387.
Evett, S.R., O’Shaughnessy, S.A., Andrade, A.A., Kustas, W.P., Anderson, M.C.,
Schomberg, H.S., 2020. Precision agriculture and irrigation: Current U.S. perspectives. Trans. ASABE. 63 (1), 57–67.
FAOSTAT, 2018. Annual Population, Republic of Korea and U.S. Food and Agricultural
Organization of the United Nations. http://www.fao.org/faostat/en/#data/OA (accessed 31 May 2021).
FAOSTAT, 2019. Production data, Republic of Korea. Food and Agricultural Organization
of the United Nations. http://www.fao.org/faostat/en/#data/QC/ (accessed 31 May
2021).
Exec. Order No. 14008, 2021. Tackling the Climate Crisis at Home and Abroad. Federal
Register 86 (19) February 1, 2021.
FAOSTAT, 2020. Food Security and Nutrition. Republic of Korea and
U.S. Food and Agricultural Organization of the United Nations.
http://www.fao.org/faostat/en/#data/FS.
Finger, R., Swinton, S.M., El Benni, N., Walter, A., 2019. Precision farming at the nexus of
agricultural production and the environment. Annu. Rev. Resour. Econ. 11, 313–335.
Fitton, N., Alexander, P., Arnell, N., Bajzelj, B., Clavin, K., Doelman, J., Gerber, J.S., Havlik, P., Hasegawa, T., Herrero, M., Krisztin, T., van Meijl, H., Powell, T., Sands, R.,
Stehfest, E., West, P.C., Smith, P., 2019. The vulnerabilities of agricultural land and
food production to future water scarcity. Global Environ. Change. 58, 101944.
Fleming, S.W., Vesselinov, V.V., Goodbody, A.G., 2021. Augmenting geophysical interpretation of data driven operations water supply forecast modeling for a western US
river using a hybrid machine learning approach. J. Hydrol. 597, 126327.
Freeman, P.K., Freeland, R.S., 2014. Politics & technology: U.S. policies restricting unmanned aerial systems in agriculture. Food Policy 49, 302–311.
Glass, V., Tardiff, T., 2021. Reforming funding of universal access to telecommunications and broadband services: Approaches for the new decade. Telecomm. Policy.
45, 102037 1-13.
Goel, R.K., Yadav, C.S., Vishnoi, S., Rastogi, R., 2021. Smart agriculture – Urgent need of
the day in developing countries. Sustain. Comput.–Infor. 30, 100512.
Gómez, C., Currey, C.J., Dickson, R.W., Kim, H., Hernández, R., Sabeh, N.C., Raudales, R.E., Brumfield, R.G., Laury-Shaw, A., Wilke, A.K., Lopez, R.G., Burnett, S.E.,
2019. Controlled environment food production for urban agriculture. Hort Sci. 54 (9),
1448–1458.
Gorli, R., G, Y., 2017. Future of smart farming with Internet of Things. J. Inform. Technol.
Its Appl. 2 (1), 27–38.
Government Republic of Korea, 2020. 2050 Carbon Neutral strategy of the Republic of Korea. Towards a sustainable and green society. https://unfccc.int/sites/
default/files/resource/LTS1_RKorea.pdf.
Gupta, A.S., Tarboton, D.G., 2016. A tool for downscaling weather data from large-grid
reanalysis products to finder spatial scales for distributed hydrological applications.
Environ. Model. Softw. 84, 50–69.
Habtewold, T.M., 2021. Impact of climate-smart agricultural technology on multidimensional poverty in rural Ethiopia. J. Integr. Agric. 20 (4), 1021–1041.
Hansen, Z.K., Lowe, S.E., Xu, W., 2014. Long-term impacts of major water storage facilities
on agriculture and the natural environment: evidence form Idaho (U.S.). Ecol. Econ.
100, 106–118.
Haqiai, I., Horeh, M., 2021. Assessment of COVID-19 impacts on U.S. counties using the
immediate impact model of local agriculture production (IMLAP). Agric. Sys. 190,
103132.
Hawng, J., Lee, S., 2015. The effect of the rural tourism policy on non-farm income in
South Korea. Tourism Manage. 46, 501–513.
He, G., Zhao, Y., Wang, L., Jiang, S., Zhu, Y., 2019. China’s food security challenge: Eeffects
of food habit changes on requirements for arable land and water. J. Clean. Prod. 229,
739–750.
Hong, E., Choi, J., Nam, W., Kim, J., 2016. Decision support system for the real-time
operation and management of an agricultural water supply. Irrig. Drain. 65, 197–209.
Hyunjin, C., 2020. A study on the change of farm using artificial intelligence focused on
smart farm in Korea. J. Phys., Conf. Ser. 1642, 012025.
Iddio, E., Wang, L., Thomas, Y., McMorrow, G., Denzer, A., 2020. Energy efficient operation and modeling for greenhouses: A literature review. Renew. Sust. Energ. Rev. 117,
109480.
Idoje, G., Dagiuklas, T., Iqbal, M., 2021. Survey for smart farming technologies: Challenges
and issues. Comput. Electr. Eng. 92, 107104.
Janowiak, M.K., Dostie, D.N., Wilson, M.A., Kucera, M.J., Skinner, R.H., Hatfield, J.L.,
Hollinger, D., Swanston, C.W., 2016. Adaptation resources for agriculture: Responding to climate variability and change in the Midwest and northeast. U.S. Dept.
This work was funded in part by the ARS RDA Virtual Laboratory
(RAVL) Program, Agreement No. 58-0210-4-001-F, Project "Application
of wireless sensor network for crop growth monitoring and irrigation
control".
References
Agricultural Improvement Act, H.R.2, 115th Congr., Public Law No: 115-334, 2018. Located at https://www.congress.gov/bill/115th-congress/house-bill/2.
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: Guidelines
for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56.
Food and Agricultural Organization (FAO), Rome, Italy.
Andrade, M.A., O’Shaughnessy, S.A., Evett, S.R., 2020. ARSPivot, A sensor-based decision
support software for variable-rate irrigation center pivot systems: Part A. Development. Trans. ASABE 63 (5), 1521–1533.
Annarelli, A., Battistella, C., Nonino, F., 2016. Product service system: A conceptual framework from a systematic review. J. Clean. Prod. 139, 1011–1032.
ANSI/ASABE Standards, 2018a. S632-1: Precision Agriculture Irrigation Language: Core
concepts, processes, and objects. ASABE, St. Joseph, Mich.
ANSI/ASABE Standards, 2018b. S632-2: Precision Agriculture Irrigation Language: irrigation system operations. ASABE, St. Joseph, Mich.
Asquith, W.H., Seanor, R.C., McGuire, V.L., Kress, W.H., 2020. Methods to quality assure, plot, summarize and interpolate, and extend groundwater-level informationExamples for the Mississippi River Valley alluvia aquifer. Environ. Model. Softw. 134,
104758 1-19.
Bae, S.J., Lee, H., 2020. The role of government in fostering collaborative R&D projects:
Empirical evidence from South Korea. Technol. Forecast. Soc. Change 151, 119826.
Baranzini, A., van den Bergh, J.C.J.M., Carattini, S., Howarth, R.B., Padilla, E., Roca, J.,
2017. Carbon pricing in climate policy: Seven reasons, complementary instruments,
and political economy considerations. WIREs Clim. Change 8, e462.
Bora, G.C., Nowatzki, J.F., Roberts, D.C., 2012. Energy savings by adopting precision agriculture in rural USA. Energy Sustain. Soc. 2 (1), 1–5.
Boursianis, A.D., Papdpoulou, M.S., Diamantoulakis, P., Liopa-Tsakalidi, A.,
Barouchase, P., Salahas, G., Karagiannidis, G., Wan, S., Goudos, S.K., 2020. Internet of Things (IoT) and agricultural unmanned aerial vehicles in smart farming: A
comprehensive review. Internet of Things doi:10.1016/j.iot.2020.100187.
Baltensperger, D., Basu, M., Dou, Z., Flis, S., Galligan, d., Matlock, M., Morgan, C… Yu, J.,
2021. The role of agricultural science and technology in Climate 21 project implementation. QTA2021-1., 1- 28. Located at https://www.cast-science.org/publication/therole-of-agricultural-science-and-technology-in-climate-21-project-implementation/.
Basso, B., Antle, J., 2020. Digital agriculture to design sustainable agricultural systems.
Nat. Sustain. 3, 254–256.
Branca, G., Perelli, C., 2020. ‘Clearing the air’: common drivers of climate-smart smallholder food production in Eastern and Southern Africa. J. Clean. Prod. 270, 121900.
Branstetter, L.G., Kwon, N., 2018. South Korea’s transition from imitator to innovator: The
role of external demand shocks. J. Jpn Int. Econ. 49, 28–42.
Bronson, K., 2018. Smart Farming: Including rights holders for responsible agricultural
innovation. Technology Innovation Management Rev. 8 (2), 7–14.
Burke, M., Emerick, K., 2016. Adaptation to climate Change: Evidence from US agriculture.
Americ. Econom. J.: Econ. Policy. 8 (3), 106–140.
Cardinale, R., 2019. Theory and practice of State intervention: Italy, South Korea and
stages of economic development. Struct. Chang. Econ. Dyn. 49, 206–216.
Carolan, M., 2020. Acting like an algorithm: digital farming platforms and the trajectories
they (need not) lock-in. Agric. Human Values 37, 1041–1053.
Castañeda-Miranda, A., Castaño-Meneses, V.M., 2020. Internet of things for smart farming
and frost intelligent control in greenhouses. Comput. Electron. Agric. 176, 105614.
Chae, C., Han, S., Cho, H., 2016. Security Vulnerability and Countermeasures in Smart
Farm. J. Digital Convergence 14 (11), 313–318. (in Korean)
Chai, Y., Pardey, P.G., Chan-Kang, C., Huan, J., Lee, K., Dong, W., 2019. Passing the food
and agricultural R&D buck: The United States and China. Food Policy 86, 101729.
Choi, J., 2006. Agricultural cooperatives in Korea. In: FFTC-NACF International Seminar
on Agricultural Cooperatives in Asia: Innovations and Opportunities in the 21st Century. Seoul, pp. 1–22.
Choi, Y., Kim, M., O’Shaughnessy, S., Jeon, J., Kim, Y., Song, W.J., 2018. Comparison of
artificial neural network and empirical models to determine daily reference evapotranspiration. J. Korean Soc. Agric. Eng. 60 (6), 43–54.
Choi, J., Lee, O., Jang, J., Jang, S., Kim, S., 2019. Future intensity-depth-frequency curves
estimation in Korea under representative concentration pathway scenarios of Fifth
assessment report using scale-invariance method. Int. J. Climatol. 39, 887–900.
Chung, S., 2002. Building a national innovation system through regional innovation systems. Technovation 22, 485–491.
Demircioglu, M.A., Audretsch, D.B., Slaper, T.F., 2019. Sources of innovation and innovation type: Firm-level evidence from the United States. Ind. Corp. Change. 28 (6),
1365–1379.
Dieter, C.A., Maupin, M.A., Caldwell, R.R., Harris, M.A., Ivahnenko, T.I., Lovelace, J.K.,
Barber, N.L., and Linsey, K.S., 2018. Estimated use of water in the United States in
2015: U.S. Geological Survey Circular 1441, p. 65. [Supersedes USGS Open-File Report 2017–1131.] https://doi.org/10.3133/cir1441.
Dimitri, C., Effland, A., Conklin, N., 2005. The 20th Century Transformation of U.S. Agriculture and Farm Policy. United States Dept. of Agriculture (Economic Information
bulletin; no. 3), Washington, D.C.
325
S.A. O’Shaughnessy, M. Kim, S. Lee et al.
Geography and Sustainability 2 (2021) 312–327
Agric. Technical Bulletin. 1944 https://www.climatehubs.usda.gov/sites/default/
files/AdaptationResourcesForAgriculture.pdf .
Jeong, H., Bhattarai, R., Hwang, S., Son, J., Jang, T., 2018. How Ångström-Prescott coefficients alter the estimation of agricultural water demand in South Korea. Water 10
(12), 1851.
Jiménez, A.F., Ortiz, B.V., Bondesan, L., Morata, G., Damianidis, D., 2020. Evaluation of
two recurrent neural network methods for prediction of irrigation rate and timing.
Trans. ASABE. 63 (5), 1327–1348.
Jivany, J., Murray, E.V., 2006. Agricultural Cooperatives of South Korea. National Bank for Agriculture and Rural Development, Mumbai Technical report
https://doi10.13140/RG.2.2.23491.94248 .
Jones, C.E., Leibowitz, S.G., Sawicz, K.A., Comeleo, R.L., Stratton, L.E., Morefield, P.E.,
Weaver, C.P., 2021. Using hydrologic landscape classification and climatic time series
to assess hydrologic vulnerability of the western U.S. to climate. Hydrol. Earth Syst.
Sci. 25, 3179–3206.
Johnston, R.Z., Sandefur, H.N., Bandekar, P., Matlock, M.D., Haggard, B.E., Thoma, G.,
2015. Predicting changes in yield and water use in the production of corn in the United
States under climate change scenarios. Ecol. Eng. 82, 55–565.
Jung, I., Bae, D., Kim, G., 2011. Recent trends of mean and extreme precipitation in Korea.
Int. J. Climatol. 31, 359–370.
Jung, H.C., Kang, D., Kim, E., Getirana, A., Yoon, Y., Kumar, S., Peters-Lidard, C.D.,
Hwang, E., 2020. Towards a soil moisture drought monitoring system for South Korea.
J. Hydrol. 589, 125176 1-10.
Kalaiarasi, S., Dharshini, V., Yagneshwaran, B., Babu, V., Kothari, P., 2018. Smart Farming
Technology. J. Network Comm. Emerg. Tech. 8 (3), 72–74.
Kamilaris, A., Kartakoulis, A., Prenafeta-Bold, F., 2017. A review on the practice of big
data analysis in agriculture. Comput. Electron. Agric. 143, 23–37.
Kernecker, M., Knierim, A., Wurbs, A., Kraus, T., Borges, R., 2020. Experience versus
expectation: farmers’ perceptions of smart farming technologies for cropping systems
across Europe. Precis. Agric. 21 (6), 34–50.
Kim, G., Chung, S., Lee, C., 2007. Water quality of runoff from agricultural-forestry watersheds in the Geum River Basin, Korea. Environ. Monit. Assess. 134 (1-3), 441–452.
Kim, S.M., Kang, M.S., Jang, MW., 2018. Assessment of agricultural drought vulnerability
to climate change at a municipal level in South Korea. Paddy Water Environ. 16,
699–714.
Kim, B.H., Kim, J.R., 2019. Implementation of smart agricultural water management system using IoT-based remote monitoring. Int. J. Adv. Sci. Techn. 28 (5), 44–52.
Kim, W., Lee, D., Kim, Y., Kim, T., Lee, W., Choi, C., 2021a. Stereo-vision-based crop
height estimation for agricultural robots. Comput. Electron. Agric. 181, 105937.
Kim, H., Yu, S., Oh, J., Kim, K., Lee, J., Moniruzzaman, M., Kim, H.K., Yun, S., 2019. Nitrate
contamination and subsequent hydrogeochemical processes of shallow groundwater
in agro-livestock farming districts in South Korea. Agric. Ecosyst. Environ. 273, 50–61.
Kim, B., Kim, M., Hahn, D., Han, K.Y., 2021b. Probabilistic flood hazard assessment
method considering local intense precipitation at NPP sites. J. Hydrol. 597, 126192.
Kim, S., Lee, K., Heo, S., Noh, S., 2020. Identifying food deserts and people with low food
access, disparities in dietary habits and health in Korea. Int. J. Environ. Res. Public
Health 17, 7936.
Kolady, D.E., Van der Sluis, E., Uddin, M.M., Deutz, A.P., 2021. Determinants of adoption
and adoption intensity of precision agriculture technologies: Evidence from South
Dakota. Precis. Agric. 22 (3), 689–710.
Klerkx, L., Aarts, N., Leeuwis, C., 2010. Adaptive management in agricultural innovation
systems: The interactions between innovation networks and their environment. Agric.
Sys. 103, 390–400.
Klerkx, L., Jakku, E., Labarthe, P., 2019. A review of social science on digital agriculture,
smart farming and agriculture 4.0: New contributions and a future research agenda.
NJAS- Wagening. J. Life Sci. 90-91, 100315.
Korea Meteorological Administration, 2020. Korean Climate Change Assessment Report
2020. KMA, Seoul.
Kranz, W.L., Evans, R.G., Lamm, F.R., O’Shaughnessy, S.A., Peters, R.T., 2012. A review
of mechanical move sprinkler irrigation control and automation technologies. Appl.
Engr. Agric. 28 (3), 389–397.
Kumar, M.D., Sivamohan, M.V.K., Narayanamoorthy, A., 2012. The food security challenge of the food-land-water nexus in India. Food Sec. 4, 539–556.
Lee, C., Chan-Olmsted, S.M., 2004. Competitive advantage of broadband Internet: A comparative study between South Korea and the United States. Telecomm. Policy 28
(9–10), 649–677.
Lee, J.M., Kwon, E.H., Woo, N.C., 2019. Natural and human-induced drivers of groundwater sustainability: A case study of the Mangyeong River Basin in Korea. Sustainability
11 (5), 1486.
Lee, N., 2019. Water Policy and Institutions in the Republic of Korea. ADBI Working Paper
985. Tokyo: Asian Development Bank Institute. https://www.adb.org/publications/
water-policy-and-institutions-korea (accessed 26 August 2021).
Lee, E.K., Zhang, W., Zhang, X., Adler, P.R., Lin, S., Feingold, B.J., Khwaha, H.A.,
Romekdo, X.X., 2020. Projecting life-cycle environmental impacts of corn production in the U.S. Midwest under future climate scenarios using a machine learning
approach. Sci. Total Environ. 714, 136697.
Lee, J.-Y., Raza, M., Park, Y.-C., 2018. Current status and management for sustainable
groundwater resources in Korea. Episodes 41 (3), 179–191.
Lee, J., Woo, J., 2020. Green New Deal policy of South Korea: Policy innovation for a
sustainability transition. Sustainability 12, 10191.
Lee, J., Oh, Oh, Y., Yoo, S., Suh, K., 2021. Vulnerability assessment of rural aging community for abandoned farmlands in South Korea. Land Use Policy 108, 105544.
Lele, U., Goswami, S., 2017. The fourth industrial revolution, agricultural and rural innovation, and implications for public policy and investments: A case of India. Agric.
Econ. 48 (S1), 87–100.
Lewis, E.C., Colón-Ramos, U., Gittelsohn, J., 2021. Food-seeking behaviors and food in-
security risk during the Coronavirus disease 2019 pandemic. J. Nutr. Educ. Behavior.
doi:10.1016/j.jneb.2021.05.002.
Li, L., Cao, R., Wei, K., Wang, W., Chen, L., 2019. Adapting climate change challenge: A
new vulnerability assessment framework from the global perspective. J. Clean. Prod.
217, 216–224.
Lioutas, E.D., 2020. Smart farming and short food supply chains: Are they compatible?
Land Use Policy 94, 104541.
Lombardi, M., Lopolito, A., Andriano, A.M., Prosperi, M., Stasi, A., Iannuzzi, E., 2020.
Network impact of social innovation initiatives in marginalized rural communities.
Soc. Networks 63, 11–20.
Marek, T.H., Porter, D.O., Howell Sr., T.A., Marek, G.W., Brauer, D., 2020. The impact
and value of accurate evapotranspiration Networks in Texas High Plains production
agriculture. Appl. Engr. Agric. 36 (4), 451–455.
Mark, T.B., Griffin, T.W., Whitacre, B.E., 2016. The role of wireless broadband connectivity on ‘Big Data’ and the agricultural industry in the United
States and Australia. Int. Food Agribus. Man. 19 (A), 43–56. https://ifama.org/
resources/Documents/v19ia/220150113.pdf.
Mehrnegar, N., Jones, O., Singer, M.B., Schumacher, M., Jagdhuber, T., Scanlon, B.R.,
Rateb, A., Forootan, E., 2021. Exploring groundwater and soil water storage changes
across the CONUS at 12.5 km resolution by a Bayesian integration of GRACE data into
W3RA. Sci. Total Environ. 758, 143579.
McLaren, C.G., Metz, T., van den Berg, M., Bruskiewich, R.M., Magor, N.P., Shires, D.,
2009. Chapter 4 Informatics in Agricultural Research for Development. Adv. Agron.
102, 135–157.
Ministry of Strategy and Finance, 2018. Agricultural innovation growth, smart farm
leads its way. Press release (April 6, 2018) (written in Korean). https://www.korea.
kr/news/pressReleaseView.do?newsId=156264600.
USGS, 2018. Next Generation Water Observing System. United States Geological
Survey | National Water Dashboard. https://www.usgs.gov/mission-areas/waterresources/science/next-generation-water-observing-system-delaware-river-basin (accessed 4 July 2021).
Ministry of Agriculture, Food and Rural Affairs (MAFRA), 2019. Smart farm R&D.
take the wings of leap Smart farm - Multi-ministerial package innovation
technology development. Press release (Oct. 29, 2019) (written in Korean).
https://www.korea.kr/news/pressReleaseView.do?newsId=156357933.
Moysiadis, V., Sarigiannidis, P., Vitsas, V., Khelifi, A., 2021. Smart farming in Europe.
Comput. Sci. Rev. 39, 100345.
Munz, J., Gindele, N., Doluschitz, R., 2020. Exploring the characteristics and utilisation
of Farm Management Information Systems (FMIS) in Germany. Comput. Electronics
Agric. 170, 105246.
Nam, W., Hong, E., Choi, J., 2015. Has climate change already affected the spatial distribution and temporal trends of reference evapotranspiration in South Korea? Agric.
Water Manage. 150, 129–138.
Nam, W., Kim, T., Hong, E., Choi, J., 2017. Regional climate change impacts on irrigation
vulnerable season shifts in agricultural water availability for South Korea. Water 9
(10), 735.
Navarro, E., Costa, N., Pereira, A., 2020. A systematic review of IoT solutions for smart
farming. Sensors 20, 4231.
Ng’ang’a, S.K., Miller, V., Girvetz, E., 2021. Is investment in Climate-Smart agricultural
practices the option for the future? Cost and benefit analysis evidence from Ghana.
Heliyon 7, e06653.
Nukala, R., Panduru, K., Shields, A., Riordan, D., Doody, P., Walsh, J., 2016. Internet
of Things: A review from “Farm to Fork”, In 2016 27th Irish Signals and Systems
Conference (ISSC). IEEE 1–6.
OECD, 2020. Agricultural Policy Monitoring and Evaluation 2020. OECD Publishing, Paris
https://dx.doi.org/10.1787/928181a8-en.
O’Grady, M.J., Langton, D., O’Hare, G.M.P., 2019. Edge Computing: A tractable model for
smart agriculture? Artif. Intellig. Agric. 3, 42–51.
O’Shaughnessy, S.A., Evett, S.R., Colaizzi, P.D., Andrade, M.A., Marek, T.H., Heeren, D.M.,
Lamm, F.R., LaRue, J.L., 2019. Identifying advantages and disadvantages of variable
rate irrigation: An updated review. Appl. Engr. Agric. 35 (6), 837–852.
Oh, K., Lee, M., J, S., 2017. Development of the Korean Climate Change Vulnerability
Assessment Tool (VESTAP) – Centered on health vulnerability to heat waves. Sustainability 9 (7), 1103 3.
Pandey, P.C., Tripahti, A.K., Sharma, J.K., 2021. Chapter 16- An evaluation of GPS opportunity in market for precision agriculture. In: Petropolous, G.P., Srivastava, P.K.
(Eds.), GPS and GNSS Technology in Geosciences. Elsevier Inc, pp. 337–349.
Pandya, J.R., Nagchaudhuri, A., Nindo, C., Mitra, M., 2019. Proceedings 2019 ASABE
Annual International Meeting. ASABE, St. Joseph, Mich, pp. 1–8 Paper No. 1900194.
Park, H.C., Oh, C.H., 2017. Flora, life form characteristics, and plan for the promotion of
biodiversity in South Korea’s globally important agricultural heritage system, the tradition Gudeuljang irrigated rice terraces in Cheonsando. J. Mt. Sci. 14 (6), 1212–1228.
Paul, C.J.M., Nehring, R., 2005. Product diversification, production systems, and economic
performance in U.S. agricultural production. J. Econometrics 126 (2), 525–548.
Pivoto, D., Waquil, P.D., Talamini, E., Spanhol-Finocchio, C.P., Dlla Corte, V.F., de Vargas Mores, G., 2018. Scientific development of smart farming technologies and their
application in Brazil. Inf. Process. Agric. 5, 21–32.
Rajabiun, R., Middleton, C., 2018. Strategic choice and broadband divergence in the transition to the next generation networks: Evidence from Canada and the U.S. Telecommun. Policy 42, 37–50.
Rotz, S., Duncan, E., Small, M., Botschner, J., Dara, R., Mosby, I., Reed, M., Fraser, E.D.G.,
2019. The politics of digital agricultural technologies: A preliminary review. Sociol.
Ruralis 59 (2), 203–228.
Sant’Ana, D., Pache, M.C.B., Borges, P.P., Dias, J.L.E., 2021. Accessibility and digital inclusion in Brazil and South Korea: A comparison between micro and macro territorial
approach. Sustain. Cities and Soc. 64, 102524.
326
S.A. O’Shaughnessy, M. Kim, S. Lee et al.
Geography and Sustainability 2 (2021) 312–327
Scanlon, B.R., Rateb, A., Pool, D.R., Sanford, W., Save, H., sun, A., Long, D., Fuchs, B.,
2021. Effects of climate and irrigation on GRACE-based estimates of water storage
changes in major US aquifers. Environ. Res. Lett. 16, 094009.
Schattman, R.E., Niles, M., Aitken, H.M., 2021. Water use governance in a temperate
region: Implications for agricultural climate change adaptation in the Northeastern
United States. Ambio 50, 942–955.
Schimmelpfennig, D., 2016. Farm Profits and Adoption of Precision Agriculture. U.S. Dept.
Agric. Economic Research Service Report No. 217.
Sebedo, D.A., Li, G., Abebem, K.A., Etea, B.G., Ahiakpa, J.K., Ouattara, N., Olounlade, A., Frimpong, S., 2021. Smallholder farmers’ climate change adaptation practices contribute to crop production efficiency in southern Ethiopia. Agron. J. 113 (6),
4627–4638.
Sedek, K.A., Osan, M.N., Omar, M.A., Wahahb, M.H.A., Idrus, S.Z.S., 2021. Smart Agro
E-marketplace architectural model based on cloud data platform. J. Physics: Conference series. 1st International Recent Trends in Engineering, Advanced Computing
and Technology Conference 1874 (1). doi:10.1088/1742-6596/1874/1/012022, RETREAT 2020.
Statistics Korea. 2020a. Agricultural Area Survey in 2020. http://kostat.go.kr/
portal/eng/pressReleases/2/2/index.board?bmode=read&bSeq=&aSeq=389047&
pageNo=1&rowNum=10&navCount=10&currPg=&searchInfo=&sTarget=title&sTxt=
(accessed 16 July 2021).
Statistics Korea. 2020b. Agriculture, Forestry and Fishery Survey in 2019.
http://kostat.go.kr/portal/eng/pressReleases/2/4/index.board?bmode=read&bSeq=
&aSeq=382929&pageNo=1&rowNum=10&navCount=10&currPg=&searchInfo=&
sTarget=title&sTxt= (accessed 16 July 2021).
Statistics Korea, 2020c. Production of Autumn Cabbages, Autumn Radishes, Beans, Apples
and Pears in 2020. Statistics Korea. kostat.go.kr. (accessed 16 July 2021).
Statistics Korea, 2020d. Production of Barley, Garlic and Onions in 2020. Statistics Korea
(kostat.go.kr) (accessed 16 July 2021)
Statistics Korea, 2020e. 2020 Population and Housing Census (Register-based Census).
http://kostat.go.kr/portal/eng/pressReleases/8/1/index.board.
Strover, S., Riedl, M.J., Dickey, S., 2021. Scoping new policy frameworks for local and
community broad band networks. Telecomm. Policy 45 (10), 102171.
Sung, J.H., Chung, E., Shahid, S., 2018. Reliability-Resiliency-Vulnerability approach for
drought analysis in South Korea using 28 GCMs. Sustainability 10, 3043.
Suttles, K.M., Singh, N.K., Vose, J.M., Martin, K.L., Emanuel, R.E., Coulston, J.W.,
Saia, S.M., Crump, M.T., 2018. Assessment of hydrologic vulnerability to urbanization
and climate change in a rapidly changing watershed in the Southeast U.S. Sci. Total
Environ. 645, 806–816.
Taghvaeian, S., Andales, A.A., Allen, L.N., Kisekka, I., O’Shaughnessy, S.A., Porter, D.O.,
Sui, R., Irmak, S., Fulton, A., Aguilar, J., 2020. Irrigation scheduling for agriculture
in the United States: The progress made and the path forward. Trans. ASABE. 63 (5),
1603–1618.
Thomasson, J.A., Baille, C.P., Antille, D.L., Lobsey, C.R., McCarthy, C.L., 2019. Autonomous technologies in agricultural equipment: A review of the state of the art.
ASABE Distinguished Lecture Series Tractor Design No. 40. ASABE, St. Joseph, Mich.
Tzounis, A., Katsoulas, N., Bartzanas, T., Kittas, C., 2017. Internet of Things in agriculture,
recent advances and future challenges. Bio Sys. Engr. 164, 31–48.
USDA-ERS, 2021. COVID-19 Working Paper: Financial Assistance for Farm Operations and Farm Households in the Face of COVID-19. Paper #AP-090. U.S. Dept.
Agric. Economic Research Service. https://www.ers.usda.gov/publications/pubdetails/?pubid=101711.
USDA-FAS, 2018. Korea’s Controlled Horticulture. U.S. Dept. Agric. Foreign Agricultural Services. Gain Report No. KS1820, Seoul. https://www.fas.usda.gov/
data/south-korea-koreas-controlled-horticulture.
USDA-FAS, 2021. COVID-19 Impact on Food Market Trends in Korea. Report
No. KS2021-0012. U.S. Dept. Agric. Foreign Agricultural Services. https://www.
fas.usda.gov/data/south-korea-covid-19-impact-food-market-trends-korea.
USDA-NASS, 2019a. Irrigation and water management survey. 2017 Census of agriculture, Vol. 3, Special Studies, Part 1 (AC-17-SS-1). USDA National Agricultural
Statistics Service, Washington, D.C. https://www.nass.usda.gov/Publications/
AgCensus/2017/Online_Resources/Farm_and_Ranch_Irrigation_Survey/index.php
(Accessed 20 May 2021).
USDA-NASS, 2019b. Farm Producers. 2017 Census of Agriculture.
USDA-ERS, 2020. Rural America at a glance. Economic Research Service. Economic Information Bulletin 221.
USDA-NASS, 2020a. Crop Values 2020 Summary. U.S. Dept. Agric.. ISSN: 19490372. Accessed on 5/31/2021 at https://downloads.usda.library.cornell.edu/usdaesmis/files/k35694332/348509606/d791t862r/cpvl0221.pdf .
USDA-NASS,
2020b.
2019
Organic
Survey.
U.S.
Dept.
Agric.
AC-17-SS-4.
Vol.
3,
special
Studies,
Part
4.
Washington
D.C.
https://www.nass.usda.gov/Publications/AgCensus/
2017/Online_Resources/Organics/ . (Accessed 21 July 2021).
USDA-NASS, 2021. Farms and Land in Farms 2020 Summary. ISSN: 19952004
https://downloads.usda.library.cornell.edu/usda-esmis/files/5712m6524/
tq57pj927/rx914h75j/fnlo0221.pdf .
Van Doi, M., Kim, J., 2020. Projections on climate internal variability and climatological
mean at fine scales over South Korea. Stoch. Environ. Res. Risk Assess. 34, 1037–1058.
Chapter 2 in Vermesan, O., Friess, P., Guillemin, P., Gusmeroli, S., et al., 2011. Internet
of Things Strategic Research Agenda. In: Vermesan, O., Friess, P. (Eds.), Internet of
Things—Global Technological and Societal Trends River Publishers, Aalborg, Denmark. ISBN 978-87-92329- 67-7.
Walter, A., Finger, R., Huber, R., Buchmann, N., 2017. Opinion: Smart farming is key to
developing sustainable agriculture. PNAS 114 (24), 6148–6150.
Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M., 2017. Big Data in Smart Farming – A review.
Agric. Sys. 153, 69–80.
Yeanjung, K., 2018. Development of Smart Agriculture Coping with the
4th Industrial Revolution. Korea Rural Economic Institute. (in Korean)
http://www.krei.re.kr/eng/researchReportView.do?key=355&biblioId=510157
&pageType=010101&searchCnd=all&searchKrwd=&pageIndex=15.
Yoon, N., Lee, J., Park, K, Lee, J., 2017. Korean smart farm policy and technology development status. Rural Resour. 59 (2), 19–27. (in Korean)
Yoon, C., Lim, D., Park, C., 2020. Factors affecting adoption of smart farms: The case of
Korea. Comput. Human Behavior. 108, 106309 1-10.
Yu, C., Miao, R., Khanna, M., 2021. Maladaptation of U.S. corn and soybeans to a changing
climate. Sci. Rep. 11 (1), 1235.
Zerssa, G., Feyssa, D., Kim, D., Eichler-Löbermann, B., 2021. Challenges of smallholder
farming in Ethiopia and opportunities by adopting climate-smart agriculture. Agriculture 11, 192.
327