Drivers and Challenges of Precision Agriculture: a Social Media Perspective

Dakota State University Beadle Scholar

Faculty Research & Publications College of Business and Information Systems

2020

Drivers and challenges of precision agriculture: a social media perspective

Martinson Ofori Dakota State University

Omar F. El-Gayar Dakota State University

Follow this and additional works at: https://scholar.dsu.edu/bispapers

Recommended Citation Ofori, M., & El-Gayar, O. (2020). Drivers and challenges of precision agriculture: a social media perspective. Precision Agriculture, 1-26.

This Article is brought to you for free and open access by the College of Business and Information Systems at Beadle Scholar. It has been accepted for inclusion in Faculty Research & Publications by an authorized administrator of Beadle Scholar. For more information, please contact [email protected]. Drivers and challenges of precision agriculture: a social media perspective

Martinson Ofori & Omar El-Gayar

Precision Agriculture An International Journal on Advances in Precision Agriculture

ISSN 1385-2256

Precision Agric DOI 10.1007/s11119-020-09760-0

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Precision Agriculture https://doi.org/10.1007/s11119-020-09760-0

REVIEW

Drivers and challenges of precision agriculture: a social media perspective

Martinson Ofori1 · Omar El‑Gayar1

Accepted: 26 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020

Abstract Precision agriculture, which has existed for over four decades, ensures efcient use of agricultural resources for increased productivity and sustainability with the use of technology. Due to the lingering perception that the adoption of precision agriculture has been slow, this study examines public thoughts on the practice of precision agriculture by employing social media analytics. A machine learning-based social media analytics tool—trained to identify and classify posts using lexicons, emoticons, and emojis—was used to capture sentiments and emotions of social media users towards precision agriculture. The study also validated the drivers and challenges of precision agriculture by comparing extant literature with social media data. By mining online data from January 2010 to December 2019, this research captured over 40,000 posts discussing a myriad of concerns related to the practice. An analysis of these posts uncovered joy as the most predominant emotion, also refected the prevalence of positive sentiments. Robust regulatory and institutional policies that promote both national and international agenda for PA adoption, and the potential of agricultural technology adoption to result in net-positive job creation were identifed as the most prevalent drivers. On the other hand, the cost and complexity of currently available technologies, as well as the need for proper data security and privacy were the most common challenges present in social media dialogue.

Keywords Social media · Precision agriculture · Smart farming · Food sustainability · Sentiment analysis · Public perception

Introduction

In recent times, Precision Agriculture (PA), a farming practice which has existed since the 1980s (Robert 2002), has become increasingly important due to food and sustainability needs, and the proliferation of smart farm technologies (SFTs) (Balafoutis et al. 2017; Kernecker et al. 2020; Wolfert et al. 2014). PA and its associated SFTs—made

* Martinson Ofori [email protected] Omar El‑Gayar Omar.El‑[email protected]

1 College of Business and Information Systems, Dakota State University, Madison, USA

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Precision Agriculture up of data acquisition technologies, data analysis, and evaluation technologies, and precision agriculture technologies (Balafoutis et al. 2017)—could be key to meeting future food demands and ensuring sustainable agriculture (Clercq et al. 2018; Wal- ter et al. 2017; Wolfert et al. 2014). In general, there is a consensus of this and other gains to be made from PA but a lingering perception on the slow adoption rate of PA still exists (Lowenberg-DeBoer and Erickson 2019), and certain instances have found the reality of farmer experience with SFTs to be diferent from their projected use by researchers and manufacturers (Lowenberg-DeBoer et al. 2019). Also, the resurgence of AI, in the form of machine learning and deep learning, as well as the existence of commercial solutions propositioned as the holy grail for yield improvement, makes it difcult to understand the reasons that hinder PA adoption. To foster PA adoption, a number of studies have been conducted in the past from various theoretical, practical, and methodological perspectives (for example, Aubert et al. 2012; Lowenberg-DeBoer and Erickson 2019; Pathak et al. 2019; Pierpaoli et al. 2013; Say et al. 2017; Tey and Brindal 2012; Wang et al. 2019). Among other factors, the studies proposed farmer age, experience, or confdence; farm soil quality, size, or sales; and even informational factors like extension services as PA adoption challenges. Although, Pierpaoli et al. (2013) in their review highlighted the importance of perception and emotional attachments as a driver for PA technology acceptance, using SM as a data source has remained unexplored. Given this context, the current study aims to complement prior research by providing a diferent perspective on PA adoption by using social media (SM) data to uncover latent insights on PA. The study mines discourse on SM platforms which include topics of difering opinions due to the sheer number of its users. In 2018 alone, SM had over 2.46 billion users, a number that represented 71% of all Internet users and about 33% of the world population (Statista 2018). With so many users, SM presents a diverse and engaging platform for people from all walks of life to contribute to matters of public opinion. This means SM adds a new dimension to human interaction through its provision of unsolicited insights. The presence of sentiments on a variety of topics ofers collective wisdom on user perception which can be a valuable indicator of current real-world performance, as well as a good estimator of future outcomes (Asur and Huberman 2010). Insights from social media mining could also inform the design and development of new technology in a manner that meets target users’ expectations and thus maximize the potential for adoption and sustained use (El-Gayar et al. 2019). Modern researchers have, therefore, sought to solve complex issues of public opinion using content analysis of SM data to understand public perception. Specifcally, the current study aims to: (1) understand the public perception towards PA, and (2) explore the drivers, and challenges of PA, with the view of providing priorities, concerns, and risk perceptions for the future of farming. The knowledge gained from the research could provide further insights into PA that can inform policies aimed at improved adoption, as well as inform future research and development of PA technologies. From a theoretical perspective, the research allows for further theory building which may subsequently result in the development of hypotheses into factors that drive or impede PA adoption. The rest of this paper is organized as follows: background of the study, methods of collecting and analyzing data, analysis of results collected, discussion, and concluded with summary fndings and implications for the future.

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Background

Despite its long history of research and practice, there exist several varying perspectives on what constitutes PA. While this was streamlined in 2019 by the International Society of Precision Agriculture (ISPA)1 with the provision of a formal defnition of PA, Lleida University (2020) lists 27 other defnitions of PA proposed over the years. Further, Lowen- berg-DeBoer and Erickson (2019) contend that distinguishing PA from other agricultural technologies such as site-specifc farming, smart farming, and digital agriculture has been problematic. Even if this could make it reasonably difcult to fully identify what this management practice entails; what the related technologies are; the topics of interest; or the impediments to its large-scale adoption, the current study takes the view that the objective of PA has remained the same over the years. As such, PA, in this study, refers to any practice that manages the spatial and temporal variability associated with agricultural soil, crops, and livestock for improved performance and sustainability with the aid of technology. Therefore, all agricultural practices, as identifed in past research, that aim to achieve this objective are considered PA and within the scope of the current study. Similarly, for the purposes of this study, drivers are defned as prevailing conditions that are most likely compel, accelerate and/or reinforce the adoption and use of PA; and challenges as prevailing conditions that could hamper or even derail PA adoption and use. The following section provides a review of a number of these drivers and challengers to PA adoption in extant literature.

Drivers and challenges of precision agriculture from an academic perspective

Table 1 provides a summary of the drivers and challenges found in the literature. In that regard, one of the drivers of precision agriculture highlighted in the literature is the mount- ing scientifc evidence that points to everyday human activity as the cause of rising global mean temperatures, which has resulted in current climatic conditions being the warmest in recorded history (Wuebbles et al. 2017). In the absence of climate adaptation, major agricultural hubs like southern Africa and South Asia could sufer the most negative impact of these temperature increases with a projected decline of up to 8% in crop yields by 2050 (Porter et al. 2014). It is also projected that a further 4 °C increase in global mean temperatures could result in declines in crop yields by as much as 50% (Porter et al. 2014). With global population growth estimates expected to exceed 60% by 2050 (IFAD 2016), and an additional 2.4 billion people expected to be living in these same regions (McCarthy et al. 2017), studies have reported that matters could be made worse by soil erosion and the lack of arable land for farming (Connolly and Phillips-Connolly 2012). Researchers believe that such issues will push for better agricultural practices that increase extreme weather resilience (Steenwerth et al. 2014). While extreme weather events are expected to drive PA adoption, policies are expected to further drive and promote the practice efectively (Steenwerth et al. 2014). Such policies should address the efect of rapid environmental change, population growth, urbanization, and the current competition for land and water resources in agriculture (Connolly and Phillips-Connolly 2012). A more synergistic approach between stakeholders is required for current eforts such as Reducing Emissions from Deforestation and Forest Degradation

1 https://www.ispag.org/. 1 3 Author's personal copy

Precision Agriculture ), and Steenwerth et al. ( 2014 ) et al. ( 2017 ), and Steenwerth et al. ), and Steenwerth ( 2008 ), and Steenwerth ( 2014 ), Hazell and Wood ( 2014 ) et al. Kshetri ( 2018 ), Pierpaoli ( 2014 ), and Misaki et al. and ( 2017 ), and Tey ( 2013 ), Saidu et al. et al. Brindal ( 2012 ) ), Misaki et al. ( 2018 ), Pierpaoli( 2014 ), Misaki et al. ( 2013 ), et al. and Brindal ( 2012 ) ( 2017 ), and Tey Saidu et al. ), Kernecker et al. ( 2020 ), and Saidu et al. ( 2020 ), Kernecker Ofori ( 2017 ) et al. ), and Wolfert et al. et al. and Brindal ( 2012 ), and Wolfert and Tey ( 2017 ) ), Walter et al. et al. ( 2018 ), Pierpaoliet al. ( 2013 ), Walter et al. ( 2019 ) et al. ( 2017 ), and Wiseman ), Wolfert et al. ( 2017 ) et al. ( 2019 ), Wolfert et al. and Steenwerth et al. ( 2014 ) et al. and Steenwerth References Connolly and Phillips-Connolly ( 2012 ), McCarthy and Phillips-Connolly Connolly ), Harvey et al. et al. ( 2012 ), Harvey and Phillips-Connolly Connolly 2020 ), ( et al. ( 2020 ), Kernecker and Ofori El-Gayar ), Harvey et al. et al. ( 2012 ), Harvey and Phillips-Connolly Connolly ), El-Gayar and ( 2012 ), El-Gayar and Phillips-Connolly Connolly ), Kernecker et al. ( 2020 ), et al. ( 2020 ), Kernecker and Ofori El-Gayar El-Gayar and Ofori ( 2020 ), Lesser 2014 Misaki and Ofori El-Gayar El-Gayar and Ofori ( 2020 ), Sykuta and Ofori ( 2016 ); Wiseman El-Gayar 2015 ), ( ( 2016a , b ), Lynch Bort et al. ( 2014 ), Connolly Grouping Driver Driver Challenge Challenge Challenge Driver Challenge Challenge Mixed - tion growth, and urbanization, competition for for and urbanization, competition tion growth, resources land and water and trade partnerships that involves new actions to actions to new partnershipsand trade that involves sustainability food promote savings to be made, availability of commercial of commercial be made, availability to savings hiring require AI that does not plug-and-play of support data availability scientists, expensive availability of training for farmers and policymak farmers for of training availability ers ment to support precision farming, availability of supportment to availability farming, precision in developing demands especially meet to energy economies driver for adoption of precision agriculture, of precision adoption clear for driver SFT adoption for added value process, and issues regarding accountability of and issues regarding process, decisions AI-based autonomous privacy of farmers, and security of farmers, privacy in this of process datacontinuous real-time collection and analysis for engagement of the youth as the next generation generation as the of the next youth engagement for of farmers Summary description Efect of rapid environmental change, rapid popula - rapid change, environmental Efect of rapid Policies that promote the use of farm technologies the technologies that use of farm promote Policies Afordability of new technology as opposed to cost cost as opposed to technology of new Afordability Lack of basic skills in handling farm technologies, technologies, of basic skills in handling farm Lack - develop infrastructural and research for Funding Rapid advancement in technology will act as a in technology advancement Rapid Trust in farm technologies, the data technologies, collection in farm Trust Ensuring the proper ownership of farm data, of farm Ensuring the the ownership proper Rapid automation and its efect on job security, push and its efect on job security, automation Rapid - Summary of drivers and challenges from the from literature Summary and challenges of drivers ment opportunities 1 Table and challenges Drivers Global climate and environmental change Global climate and environmental Governance, policies, and trade opportunities policies, and trade Governance, Cost and complexity of Technologies and complexity Cost Education and training Infrastructure and investment Technology as an enabler Technology Trust and accountabilityTrust Data ownership, privacy, security, and transparency security, privacy, Data ownership, Automation efects on human capital and employ Automation

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(REDD +), National Adaptation Programmes of Action (NAPAs), Nationally Appropriate Mitigation Actions (NAMAs) to boost agriculture and raise awareness among policymakers (Harvey et al. 2014; Steenwerth et al. 2014). Similarly, the importance of interdiscipli- nary and transdisciplinary scientifc research in adaptation strategies and agriculture policy formation cannot be understated (Steenwerth et al. 2014). Evidence-based policies backed by governments will further resilience, reduce risks that bar farmers from PA adoption, and increase cross-border trade (Connolly and Phillips-Connolly 2012; Steenwerth et al. 2014). It is unsurprising that countries in the Organization for Economic Cooperation and Devel- opment (OECD), such as the US and Canada, are being protected by policies that safe- guard their local markets and increase their export opportunities, while others in developing economies—who often lack similar policies—continue to lose substantial market share for their traditional exports (Hazell and Wood 2008). Regardless of the need to preserve the environment or follow government regulation, researchers assert that what farmers are looking for is a way to improve their bottom-line, that is to say, increase proftability and efciency while reducing costs (Tey and Brindal 2012; Wolfert et al. 2017). However, for both adopters and non-adopters of SFTs, the complexity of such systems and the difculty of interpreting the data presented is a huge barrier to adoption (Kernecker et al. 2020; Kshetri 2014). Data scientists could be benefcial to agriculture, especially in this age of big data, but given the current competitive market for their services, they have become some of the most expensive talents to hire (El- Gayar and Ofori 2020). For most rural dwellers and small scale farmers, the situation is often worsened by the cost of buying and servicing both hardware and software (Misaki et al. 2018; Saidu et al. 2017). Therefore, the cost and complexity of these systems are a barrier to adoption and use in PA. In the meantime, it has been proposed that a solution to this conundrum is to reduce the barriers to entry through the provision of inexpensive and agriculture-friendly commercial applications that require little computer or statistical knowledge (El-Gayar and Ofori 2020; Kernecker et al. 2020). That is to say, reducing the complexity of the SFTs could bolster farmer confdence and consequently result in SFT adoption (Pierpaoli et al. 2013). Another challenge worth noting is that some countries use outdated agricultural curricu- lums, and in most developing economies, there are still no Internet-based farming knowl- edgebases available in their local language (Saidu et al. 2017). Since having the technology is not half as important as knowing how to use it, this lack of technical education and language barrier on current PA practices often leads to farmers’ refusal to adopt SFTs (Misaki et al. 2018; Saidu et al. 2017). Further, formal education has been found to be positively related to the adoption of PA (Pierpaoli et al. 2013; Tey and Brindal 2012). It has also been argued that the issue of lack of education transcends farmers—as evidenced by the decline in fnancial support for educational programs—and there is a need to raise awareness among policymakers and other stakeholders on what current technologies are, and to what extent they can solve agricultural issues (Harvey et al. 2014). Connolly and Phillips- Connolly (2012) and Saidu et al. (2017) extend this conversation and request academicians to invest more research eforts into developing programs that support the efcient imple- mentation of agricultural technology with special attention to farmers’ organizations and women. Further, they recommend research only as a starting point. In addition to educational reforms, there are challenges with infrastructure which need to be addressed to facilitate the adoption of PA. Failure to adopt SFTs have been attrib- uted to the threat of high investment costs (Kernecker et al. 2020). In the case of developing economies, where most agricultural products are sourced, the lack of investment often extends to supporting infrastructure. Power supply and Internet connectivity in these 1 3 Author's personal copy

Precision Agriculture countries are often sporadic; computers and other infrastructure are woefully inadequate; and there is often low coordination of stakeholders due to institutional diversity and depart- ment disintegration (El-Gayar and Ofori 2020; Misaki et al. 2018; Saidu et al. 2017). In the case of developed economies, failure to reinvest in existing agricultural infrastructure often leads to the loss of competitive advantage (Connolly and Phillips-Connolly 2012). If the challenges of low education and lack of infrastructural investments are tackled appropriately, then the rapid advancement in technology could, in itself, be a major driver for their introduction into agriculture. The ever-decreasing cost of infrastructure such as cloud services, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastruc- ture as a Service (IaaS), coupled with the recent resurgence of artifcial intelligence will allow farmers to essentially focus on being farmers while merely leveraging these systems to improve yield (El-Gayar and Ofori 2020). The Internet of Things (IoT) and big data will cause the need for radical changes to stay on top of decision-making (Wolfert et al. 2017). As more industries—healthcare, aviation, entertainment—embrace drones, robotics, sensors, and other technologies as a mainstay, farmers will be pushed to gradually adopt them. As adoption of PA relies on the farmer perception of both proftability and efort required to operate SFTs, these technologies will only become a must-have if technology developers improve their efciency, reliability, reduce complexity, and show that there is clear added value in their use (Kernecker et al. 2020; Tey and Brindal 2012). Currently, to better understand and create value from the often complex, multivariate, and unpredictable agricultural ecosystems, analysis of big agricultural data is required (Kamilaris et al. 2017; Wolfert et al. 2017). Lesser (2014) is of the view that a key issue when it comes to farmers’ data is trust and that the main fear for most farmers is that their farming techniques and strategies could be shared with competitors or even the larger play- ers such as Monsanto. Further, several issues of mistrust between farmers and other stakeholders such as service providers, project organizations, and even government agencies have been uncovered in research (Misaki et al. 2018; Wiseman et al. 2019). Also, there are issues with the explainability of current AI solutions. The same ethical issues the auto- mobile industry faces with self-driving cars are evident in the agricultural industry. The lack of explainability behind automated decisions raises questions of accountability that challenge PA adoption and use (El-Gayar and Ofori 2020). For example, “who is respon- sible for traces of fungicides left behind on harvested fruits when that fungicide has been applied too late? Is it the farmer, the provider of the software, or the producer of the sensor?” (Walter et al. 2017). Still on the issue of data, however, research shows that most farmers are unaware of the terms that govern the ownership, use, and access to farm data (Wiseman et al. 2019). This apparent lack of transparency regarding data ownership, portability, privacy, trust, distribution costs, and liability between technology providers and farmers has led to further perception issues that fuel farmers’ reluctance to wholly embrace SFTs (Sykuta 2016; Wise- man et al. 2019; Wolfert et al. 2017). Such a challenge to PA adoption begs the question as to why the agricultural industry does not have robust regulations surrounding the privacy and security of farm data as has been done with the Gramm-Leach-Bliley Act of 1999 (GLBA) for fnancial data and the Health Insurance Portability and Accountability Act of 1996 (HIPAA) for health data (El-Gayar and Ofori 2020). On another hand, there have been suggestions that employing SFTs could mean letting go of farmhands. There exists a perpetual fear that technology proliferation—especially IoT, AI, and big data—will result in several redundant jobs. Technology leaders such as Bill Gates and Stephen Hawking have claimed that, in 20 years, the labor market will replace several jobs with software automation (Bort 2014; Lynch 2015). Research shows 1 3 Author's personal copy

Precision Agriculture that the number of people employed in agriculture has been in decline since the 1950s (Roser 2020). In efect, software automation could pose a serious challenge to agriculture if not treated with all the seriousness it deserves. However, research into this issue has indicated that automation could also be a driver for the industry rather than a challenge as it provides an avenue for the youth to engage in farming (Connolly et al. 2016a, b; Steen- werth et al. 2014).

Uncovering insights with social media analytics

Social media (SM) has emerged beyond a platform meant to connect users with friends and family to one that challenges mainstream narratives. In this way, it has become an avenue for intellectual and academic discourse (George 2011; Jackson et al. 2001), a source of news (Kwak et al. 2010), and for marketing, advertising, and promotions (Hanna et al. 2011). This situation makes SM a viable source for researchers to gather unsolicited insights and analyze them to identify issues that afect people from diverse backgrounds. For example, to fnd sentiments on the new release of two new products from Sam- sung and Apple, SM was used to analyze early reactions to both products by conducting topological analysis on Twitter streams (Lipizzi et al. 2015). Further, Runge et al. (2013) used the SM platform—Twitter—to map the nanotechnology landscape in the U.S. Their study, which claims to be the frst systematic and exhaustive analysis on nanotechnology tweets over a specifc period, also analyzed the certainty and optimism surrounding those tweets. Similarly, Bian et al. (2016) mined Twitter to understand the public perception of the Internet of Things (IoT). In their paper, they generated trends of topics on IoT and validated them with Google trends. Also, they performed sentiment analysis to gain insights into what the attitude of the public is towards IoT. More recently, El-Gayar et al. (2019) examined the factors that infuence the adoption and use of wearable devices by exploring the perception and reactions of Twitter users. Their analysis identifed user priorities for device design that could inform the development of the devices and their integration into other ecosystems. SM has been identifed as a rich source for discussing agricultural issues. Stevens et al. (2016) revealed that the presence of mass self-communication on social media is a nascent force that can disrupt agro-food governance and create a resource for stakeholder management. To this end, Choi (2016) leveraged SM to understand severe weather events in Argentina and measure its impact. Also, Saravanan and Perepu (2019) used SM to develop a framework that combines text and image data to suggest a solution to farmer queries posted online. On the other hand, Connolly et al. (2016a, b) used SM data to validate their GLIMPSE framework developed 5 years earlier. They employ a social media analytics tool—Crimson Hexagon—to model word clusters allowing them to visualize their framework in action and, in extension, identify barriers facing farmers. This study aims to complement extant research regarding the drivers and challenges to PA adoption with an explicit account of public insight into the practice. For example, the fear of losing out on competitive advantage presents a complex relationship between farmers and their data—especially regarding farming practices. However, the difcult task of measuring the true feelings of both farmers—and consumers—on PA can be done using social media data. In efect, social media serves a rich data source that spans the entire agricultural and food production chain. This study, therefore, complements the literature by exploiting publicly available user-generated content on social media to uncover public perception and, in extension, validate drivers and challenges of PA. 1 3 Author's personal copy

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Methods

This exploratory study employs a mixed-method content analysis approach to collect, analyze, and interpret social media posts and place them in the context of PA literature. Con- tent analysis, often used in social sciences to study the content of communication, is a research technique used to analyze text data in a given context (Krippendorf 2013; Robson 2002). The use of content analysis allows researchers to document unobtrusively, closely read, and interpret social media text into the analytical narratives present in this study (Robson 2002). The specifc approach used is outlined in the ensuing section.

Data sources

The study leveraged the Crimson Hexagon’s (CH) ForSight platform (now Brandwatch), an automated nonparametric content analysis tool used to collect and analyze online data (Crimson Hexagon 2018a). The platform has access to a variety of social media platforms including Twitter. In this paper, the data sources used consisted of user-generated public content from social media sites such as:

• Twitter CH’s has a partnership with Twitter which guarantees the ForSight platform access to 100% of all posts that match the given criteria. This access, commonly known as the Twitter Firehose meant posts for this study were not subjected to sampling but consisted of a whole stream of tweets that matched the search query within the speci- fed date range. • Reddit CH also partners Reddit and, as such, has access to over 1.4 billion posts collected since May 2008 and a further 80% of Reddit data added every week. • Forums These are thread-based discussions in which users post or partake in topics of interest (such as Baidu, Yahoo!, LinkedIn, The Farming Forum).

In acquiring data from these sources, the current study takes into consideration all the common regulatory concerns that arise with social media research. Specifcally, the study conforms with federal regulations on research about human subjects by using only public information that requires no interaction with the individual posting (Moreno et al. 2013). Further, the use of CH ensured that the study conformed with all the common ethical questions raised when performing web mining (Krotov and Silva 2018).

Data collection

The study collected English-language posts published between January 2010 and Decem- ber 2019 from the sources mentioned above. This period was practical given that it coin- cides with both the proliferation of social media and recent advancements such as big data (Wolfert et al. 2017). Table 2 summarizes the search query used to capture SM discourse on PA. First, the combination of two groups of keywords—pseudonyms of Precision Agri- culture in Group 1 combined with other terms that refer to the practice of farming in Group 2. Examples include:

• Site-Specifc Management (Robert 2002) • Precision Agriculture (Lowenberg-DeBoer and Erickson 2019; Pathak et al. 2019; Robert 2002) 1 3 Author's personal copy

Precision Agriculture Relation ((Group 1 AND Group 2) 1 AND Group ((Group 3)) OR (Group 4) (Group NOT - digital tuto coins, free farm digital currency, farm NileUniversity, nowplaying, rial, add me, Farmville sionagric*, digitalfarm*, digitalagric*, variableratefarm*, sitespecifcfarm*, sitespecifcagric*,remotesensingfarm*, variablerateagric*,- remotesensingag ric* digital farm animals, shazam, tomlinson, louis*, thisisfusion, hell, rexha, digital louis*, thisisfusion, animals, shazam, tomlinson, hell, rexha, farm agriculture 4.0, agriculture4.0, smartfarm*, smartagric*,- preci precisionfarm*, farming, farm, agriculture, farm, farming, agric, agro Keywords precision, smart,precision, sensing digital, remote rate, site specifc, variable Group 4 Group Group 3 Group Group 2 Group Group Group 1 Group Social media search criteriaSocial media search Noise and/or Of-Topic Noise Possible Tags Possible 2 Table Theme Alternate Name

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• Smart Agriculture (El-Gayar and Ofori 2020; Wolfert et al. 2017, 2014) • Digital Agriculture (CEMA - European Agricultural Machinery 2017) • Variable Rate Farming (Pathak et al. 2019) • Remote Sensing Farming (Pathak et al. 2019)

Second, through an iterative process, hashtags and other metatags were captured on sites like Twitter, which due to its 140-character limit allows users to creatively combine words for efect (Efron 2010). This group of words was treated as Group 3. The review excluded potential noise from the result using keywords from Group 4. These keywords were specifcally created to avoid of-topic discussions such as the music group Digital Farm Animals and the online game Farmville. It must also be noted that in all scenarios and whenever possible, wild card searches that combine a root term with the asterisk (*) symbol was used to capture variants of the search phrases. For example, a search for precisionfarm* could return posts that contain precisionfarm, precisionfarms, precisionfarmer, or precisionfarm- ing, and any other variants of the word that uses the same root keyword. To summarize, the groups identifed above were combined to capture posts that contain:

• A keyword each from Group 1 and Group 2 excluding all keywords from Group 4 OR • A keyword from Group 3 excluding all keywords from Group 4

Data analysis

Conceptually, this study was designed with two research goals in mind. The frst, Goal 1, was to explore the perception of precision agriculture. Goal 2, on the other hand, was designed to investigate the relative prevalence of the underlying subjects pushing or hin- dering the industry by leveraging extant literature. Using CH, the analysis was, similarly, carried out in two phases. For Goal 1, sentiment and emotion analysis was conducted. Sentiment and emotion analysis uses natural language processing and text analysis techniques to identify and classify polarity, human emotion, opinion, and subjectivity of a piece of text (Bakshi et al. 2016). This is done by training a machine learning algorithm to identify the frequency distribution of words, negated words, and lexicons for classifcation purposes. In most cases, sentiment and emotion analysis are also aided by the presence of emoticons and emojis which augment text with non-verbal elements to convey the authors feeling or mood (Novak et al. 2015). For example, characters such as “(Y)” and “ ” –represent shorthand for agreement and hence express positive sentiment. Similarly, the use of “:-)” and “ ” expresses a joy emotion. In this study, this task was done using CH’s default Buzz monitor. Buzz monitors on CH are pre-trained classifers that perform automatic word clustering, sentiment, and emotion analysis. Sentiment analysis uses a vast dataset of over 500 000 posts previously hand-labeled as positive, negative, or neutral (Crimson Hexagon 2019c). Emotion analysis captures the underlying feeling in posts for six emotional categories. The categories of emotion are based on Ekman 6—Anger, Fear, Disgust, Joy, Surprise, and Sadness (Crimson Hexagon 2018b; Ekman 1992). The second phase of the study aimed at addressing Goal 2 was tackled as an iterative process using an Opinion monitor in CH. An Opinion monitor uses BrightView—a supervised machine learning classifer that is based on the algorithm developed by Daniel 1 3 Author's personal copy

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Hopkins and Gary King (Hopkins and King 2010). Brightview allows researchers to create customized categories for unstructured text by manually hand-labeling posts into pre- defned categories. In this study, the categories were established using the drivers and challenges identifed in the literature review and summarized in Table 1. A codebook was created to guide the process of clearly delineating the boundary of each category (see Table 5 in Appendix). The researchers trained the BrightView classifer on CH by providing at least 50 posts per each category for training.

Results

A total of 44 937 online posts between the review period, January 2010 to December 2019, were mined for analysis. The user-generated content mined consisted of 34 035 (75.8%) Twitter posts, 9177 (20.4%) forum discussions and 1699 (3.8%) additional posts from Red- dit. Figures 1 and 2 show the source distribution and time distribution of posts per date, respectively. As displayed in Table 3, a total of 33 666 posts with an identifable location were included in this study. Only 7 countries had more than 1000 posts: United States (10 862), United Kingdom (4257), Kenya (2716), Nigeria (2551), India (1771), Canada (1201), and Australia (1029). These posts accounted for 72.44% of the geographically identifable posts. The remaining countries that made up the top 15 were South Africa (869), the Republic of Ireland (529), Zimbabwe (471), Ghana (455), Belgium (445), Netherlands (443), Germany (422), and Italy (391). Using sentiment and emotion analysis, a total of 38% of posts showed sentiment (17 255 of posts) and 41% of posts showed emotion (18 513 of posts). Figure 3 shows the

Fig. 1 Source distribution

Fig. 2 Time distribution of post

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Table 3 Summary statistics of Rank Country # of posts % of total posts geographical information 1 United States of America 10 862 32.26 2 United Kingdom 4257 12.64 3 Kenya 2716 8.07 4 Nigeria 2551 7.58 5 India 1771 5.26 6 Canada 1201 3.57 7 Australia 1029 3.06 8 South Africa 869 2.58 9 Republic of Ireland 529 1.57 10 Zimbabwe 471 1.40 11 Ghana 455 1.35 12 Belgium 445 1.32 13 Netherlands 443 1.32 14 Germany 422 1.25 15 Italy 391 1.16 16 Uganda 349 1.04 17 Panama 347 1.03 18 Pakistan 289 0.86 19 Indonesia 280 0.83 20 Rwanda 265 0.79

Fig. 3 Sentiment and emotion analysis from social media

percentages of positive and negative sentiments as well as the percentage of various emotions contained in these posts. Using two out of four available visualizations, a topic analysis was performed to identify the relevant underlying themes and ideas from the posts gathered. Figure 4 is a topic wheel which identifes groups of recurring words and phrases in the conversations arranged as topics and subtopics (Crimson Hexagon 2019a). Figure 5 is a word cluster that arranges words as interconnected bubbles of relationships from a random sample of up to 10 000 posts within the review period (Crimson Hexagon 2019b).

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Fig. 4 Prevalent topics and subtopics

Fig. 5 Word clusters of prevalent topics

The results presented in this portion of the study uses only SM data. The groupings outlined in Table 1 and the codebook from Table 5 in Appendix served as a guide for categorizing posts. The analysis of posts, which used CH’s BrightView algorithm, categorized the relevant conversations into 51.5% (21 518) drivers and 48.5% (20 273) challenges. Table 4 shows the number and percentage of posts in each category while Fig. 6 depicts the relative prevalence of each topic within its respective class (as a driver or a challenge). For drivers, conversations regarding governance and policies, and automation made up more than 66.2% of the conversations on drivers. Concerning challenges, issues with data ownership

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Table 4 Topic prevalence of the drivers and challenges from social media Drivers and challenges Grouping # of posts % prevalence

Governance, policies, and trade opportunities Driver 7359 17.61 Automation efects on human capital and employment Driver 6908 16.53 opportunities Data ownership, privacy, security, and transparency Challenge 6891 16.49 Global climate and environmental change Driver 4413 10.56 Cost and complexity of Technologies Challenge 4208 10.07 Education and training Challenge 4066 9.73 Technology as an enabler Driver 2838 6.79 Trust and accountability Challenge 2645 6.33 Infrastructure and investment Challenge 2462 5.89

Fig. 6 Drivers and challenges of precision agriculture

and security coupled with the cost and complexity of SFTs also made up about 55% of the conversations on challenges.

Discussion

The results presented above indicate an increasing trend in online mentions of PA. This growing trend starts to peak in mid-2014, coinciding with the launch of the Global Alliance for Climate Smart Agriculture (GACSA) at the UN Summit on Climate Change (https://www. fao.org/gacsa/en/ ). Although GACSA enjoyed only moderate mentions in social media, the extensive coverage on it in news and blogs may have contributed to the spark in discussions on agriculture and climate change.

Public perception of precision agriculture

It is often important to consider the emotional attachments consumers have towards new technology (Karahanna and Straub 1999; Read et al. 2011). As noted by Pierpaoli et al. (2013), studies into the adoption of PA should not overlook the importance of perception as an

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Precision Agriculture important driver for behavior towards the practice and intention to purchase associated technology. Sentiment and emotion analysis associated with online discourse on PA seems to support the narrative that PA will be valuable for climate management and food sustainability. This is evident from the presence of joy as the predominant emotion displayed in the posts, as well as the primarily net positive sentiment on PA. Although, the main goal for this part of the study was to identify the underlying feelings of emotion displayed in publicly available posts, themes from the literature show up as posts are investigated. For example, joy posts, also often express positive sentiment and highlighted mostly perceived drivers while other emotions, such as Anger and Sadness, often alluded to the challenges of PA (see examples in Table 6 in Appendix). As an emerging agricultural management concept, PA was known as site-specifc management (SSM) (Robert 2002). Although the current name, Precision Agriculture, has become the more akin to the practice, the use of recent underlying technologies has seen a shift in the naming convention with some preferring the term Smart Agriculture (SA) or Smart Farming (SF). Some researchers contend that the main diference between PA and SA is that, with the help of AI and big data, the latter goes beyond in-feld variability to enhance management decisions based on context and situation awareness (Wolfert et al. 2017, 2014). This notion holds for some social media users as the mention of SA often came with the reference to underlying technologies of SFTs. Figure 5 illustrates some of these technologies in the word clusters. Interestingly, the infographic also shows three other main themes formed organically from the discourse on PA—yield consistency and/or increase; addressing or fnding a solution to food security; and reducing climate change. This provides further evidence that the SM users understand what PA is, as well as the benefts it claims to bring. It also sets the tone for accepting and valuing the contributions of SM users. Another concept often mentioned in posts as key to the future of food sustainability is Climate-Smart Agriculture (CSA). In the face of climate change, CSA aims to develop technical, policy, and investment conditions in reaching sustainable agricultural development for food security (FAO 2020; Preissing et al. 2013). Additionally, CSA has three main objectives: sustainably increasing agricultural productivity and incomes; adapting and building resilience to climate change; and reducing greenhouse gas emissions (FAO 2020). While PA and SA are often referred to as management practices, CSA is an approach that builds on these by explicitly focusing on climate change while considering productivity, adaptation, and mitigation; and assisting to close investment defcits (World Bank 2020).

Drivers and challenges of precision agriculture

This section discusses the drivers and challenges of PA. Starting from the most prevalent in each category, the discussion is based on an analysis of each group of modeled posts in the context of extant literature.

Drivers

Governance, policies, and trade opportunities From a majority of the social media posts (17.61% of total posts analyzed), agriculture is expected to beneft from robust regulatory and institutional policies that promote both a national and international agenda for PA adoption. The US, for example, is expected to beneft from their proactivity in the passing of

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Precision Agriculture the Precision Agriculture Connectivity Act of 2018 to provide broadband internet access to farmers in rural areas, a move that is seen as the next step in revolutionizing the sector (Latta 2018). The optimism of SM users in countries that have implemented such policies, and the incessant calls by those in countries that have not done the same, shows that adapting similar policies on a global scale will drive further engagement in PA. Further analysis also revealed that for SM users, well-developed policies, especially in developing economies, are required to increase access to markets in developed countries while reducing their over-reliance on subsidized imports. Findings in this category supported the literature that better cooperation amongst policymakers and stakeholders in all industries—trade, fnance, technology—will promote a drive for sustainable development. There was also support for the Harvey et al. (2014) call for the involvement of PA stakeholders in all discussion geared towards developing climate strategies and policies. The ‘Precision Agriculture Connectivity Act of 2018’ has been introduced in the USA. It will bring together the USDA, the FCC & public & private stakeholders to address the needs of precision agriculture & to address gaps in coverage #fxbushin- ternet [external link]

Automation efects on human capital and employment opportunities As countries go up the development curve, the population working in agriculture tends to decline (Roser 2020). Further, Roser (2020) has discussed how agriculture employs two-thirds of the population in developing countries compared to 5% or less in rich countries. In a careful analysis of the SM posts in this category, there was no clear indication that the shortage of manual labor will result in agricultural process automation. However, for about 16% of the total SM posts analyzed, the current fascination with technology—which has resulted in projections of up to an 18% increase in the number of jobs in the U.S alone by 2022—will boost the use of SFTs and increase employment in the sector. These SM users agreed with Steenwerth et al. (2014) and Connolly et al. (2016a, b) that while the replacement of human labor by SFTs pose a growing concern for many, it will increase rather than reduce employment in the sector. This means automation could be a major driver for technology jobs as a replacement for on-farm manual labor. It is envisioned that as manual labor continues to decline, the use of SFTs will result in net job creation especially for the younger generation due to the increased emphasis on technology education in schools. Global #food demand is expected to increase anywhere between 59 to 98% by 2050, but arable land size is eroding. Use of #Robotics, and #AI as tools of #precision farming has potential to address the demand, while also creating high-end farming #jobs–net job creation possibility

Global climate and environmental change Site-Specifc Management, Precision Agri- culture, Smart Farming, and Climate Smart Agriculture all play a part in agricultural practices that have a key goal—reducing the efect of agriculture on the environment through the precise application of chemicals such as fertilizers, pesticides, and more (Wolfert et al. 2014). Consistent with this notion, climate-smart agriculture is seen as the key to increasing productivity and resilience, reducing greenhouse gases (GHG), and enhancing food security. For these reasons, the World Bank and its partners have released about 30 CSA country profles since 2014 that discuss country-specifc challenges and how to adapt them to climate change mitigation (World Bank 2019). The next phase in their plan will result in further collaboration with partners like the Adaptation for African

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Precision Agriculture

Agriculture (AAA) Initiative, the International Center for Tropical Agriculture (CIAT), and the International Institute for Applied Systems Analysis (IIASA) to create the CSA Investment Plans (CSAIPs). The CSAIPs will identify concrete actions governments can take to boost climate-smart agriculture. With enough support initiatives like these will result in a major drive for PA adoption across the globe. About 4000 posts in the result analyzed made this evident and indicated that extreme weather and climate events, such as heat waves and droughts, will lead to the need to adopt PA. Under optimistic lower-end projections of global warming, climate change may reduce crop yields by between 10‒20% in most parts of Africa. Adopting smart agriculture is the best way to ensure a food secure future. #SmartAgricKE

Technology as an enabler With the introduction of the IoT and Cloud computing, systems are generating and/or collecting unprecedented amounts of data (Clercq et al. 2018; El-Gayar and Ofori 2020). Researchers and commercial application providers are training AI algorithms to retrieve value from systems such as remote sensing satellites, sensors, drones, agricultural robots, and other farming equipment (Özdemir and Hekim 2018; Weltzien 2016). Consistent with various studies (El-Gayar and Ofori 2020; Kernecker et al. 2020), it is envisioned by a smaller section of SM users that farmers will be enticed to adopt smart farming to stay competitive in the face of the continuous improvements to these systems will bring especially in the areas of yield improvements and competitive pricing. […] advances in information and communication technologies in traditional industries is a frequently untapped source of value added that has the potential to propel early adapters towards acquiring global competitive advantages. Such a potential is clear today in agriculture, where ICT advances are enticing businesses to rethink their processes along the full value chain […]

Challenges

Data ownership, privacy, security, and transparency Data in agriculture, as with other industries, is relevant more than ever. With all its apparent usefulness, however, comes several crosscutting issues regarding the issue of ownership, privacy, and security. Having by far the most discourse in the challenges section (17% of total posts analyzed), SM users posted that farmers are efectively owners of their data. With this data ownership, coupled with the availability of enabling technologies, farmers will gain competitive advantage. In agreement with El-Gayar and Ofori (2020), the view is that agriculture will beneft from its own data regulations. Such regulations should tackle efectively ownership, privacy, security, and transparency in sharing data—regulations similar to that of the Health Insurance Portability and Accountability Act of 1996 (in health), Gramm-Leach-Bliley Act of 1999 (in fnance), and the European General Data Protection Regulation (GDPR). Data collected in precision farming has great value and potential. It’s your data and you own it as the farmer.

Cost and complexity of technologies This category which makes up the second most prevalent challenge discussed on SM with a little over 10% of the analyzed posts and was made up of other overlapping issues that could, otherwise, form their own categories. Like 1 3 Author's personal copy

Precision Agriculture that of data, these issues come with the proliferation of technology. SM users often viewed PA and SFTs as expensive, too complex, or even lacking support, especially for smallholder farmers in developing countries, a view that is shared by prior research(Misaki et al. 2018; Saidu et al. 2017). With so many options to choose from, there are huge opportunities for farmers to adopt technology but until issues such as these are tackled efectively, they will continue to remain a challenge. […] Interesting quote “Farmers in both the US and EU struggle with the adoption of Precision Agriculture. The overall complaint is that the technology is too expensive, too complex and doesn’t have a reasonable outlook on a return on investment. #blairs #simpletruth #letsbehonest […].

Education and training The limited availability of proper education and training opportunities will continue to be a challenge for farmers, especially in developing countries, unless they are tackled properly. This is a point that has been reiterated by several researchers especially those dedicated to discussing various aspects of farming challenges (Harvey et al. 2014; Kshetri 2014; Misaki et al. 2018; Saidu et al. 2017). This view is confrmed as not being one-of by analyzing SM posts. To this end, it is recommended that similar exhaustive eforts used to develop new technology should go into educating and training farmers. More than empty and trivial donation of fertilizers to farmers, we need professional extension services. Farmers must be included in programmes that empower them for the task. Climate education, crop improvement researches, smart farming… #WorldFoodDay

Trust and accountability The issue of trust has been raised several times in research because it touches so many layers of farming. Research often alludes to farmers’ belief that their data could be used to their disadvantage (market manipulation, farming techniques, and others.) (Lesser 2014; Sykuta 2016). There is also the issue of farmers who lack awareness and would rather trust their gut rather than their data or decisions made by an automated system (El-Gayar and Ofori 2020). The data analysis confrmed the above in about 2600 SM posts that indicated trust-building as an essential ingredient to technology adoption in farming settings. What’s the role of #climateservices in adapting to climate change impacts in #agriculture? & how can we build trust on the predictions?

Infrastructure and investment Data scientists and commercial application developers are working to develop solutions to agricultural problems. Power supply and Internet access are often needed for the efective use of such solutions, are often lacking, especially in developing economies (El-Gayar and Ofori 2020; Saidu et al. 2017). Although some of the SM data implied that there was still a lot to be done, it could also be seen that investment was on the rise in the sector. In fact, after 2015, the data shows an increase in the number of investments from global stakeholders such as the United Nations’ (UN) Food and Agriculture Organization (FAO), and the World Health Organization (WHO), the World Bank, as well as, local governments. If such investments continue and are channeled towards developing the appropriate supporting infrastructure, this category could become a driver for the industry rather than the challenge it currently is.

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World Bank commits to step up climate fnance to $100 billion, half of which will go to build better adapted homes, schools and infrastructure, and invest in climate smart agriculture, sustainable water management and responsive social safety nets #COP24 #ClimateAction

Limitations and future implications

As have been highlighted by Lowenberg-DeBoer and Erickson (2019) and noted earlier, the difculty in distinguishing PA from other terms describing agricultural technology meant this study had to use a combination of these terms to collect SM posts. While this helps in providing an all-encompassing view of the feld, the researchers acknowl- edge how this could afect the results and interpretation of the same as presented in the discussion. Additionally, it is important to highlight the issues raised by past studies on social media research such as the presence of a primarily younger population and the propensity for over- or under-coverage of certain topics on SM (Di Consiglio et al. 2018; Wojcik and Hughes 2019). Further, some drivers and challenges of PA adoption that may be difcult to model using social media data were also left out of the current study. This includes but not limited to social factors such as the age and experience of the farmers; and institutional and fnancial factors such as the use of forward contracts, tenure systems, and farm regions (Pierpaoli et al. 2013; Tey and Brindal 2012). Regardless, a few limitations of this study also create opportunities for future research. For example, while the creation of a codebook streamlined the process of cat- egorization, online posts are often not mutually exclusive. This means posts that fell in multiple categories and not used in training were still classifed into a specifc category based on keyword prevalence. These were, however, corroborated by the authors, and posts were re-classifed where necessary. While this is a system limitation on CH, future researchers may consider other SM analytics platforms or other statistical methods to categorize posts. Moreover, the platform limitation highlighted did not allow an analysis of news media and blog posts. Future research may also beneft from analyzing these sources as they have been found to be more prevalent than social media discourse on PA (Ofori and El-Gayar 2019). As has been done in past research of similar nature (Williams et al. 2015), social media networks can be characterized by diferent types of social ties that foster interaction with like-minded people and communities and could in turn facilitate or constrain discussions on such topics. Future studies could use quantita- tive techniques to analyze the structure of networks and correlate the results with those uncovered in discourse. This study is a baseline study aimed at providing insights into PA and refect the feelings of the public on this concept. Regardless, future studies might beneft from an assessment of both the quality of the information sources and the information itself. Fur- ther analysis of how such information infuences causal beliefs in the form of farming decisions or even adoption of farm technology is required for further theory building.

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Precision Agriculture

Conclusion

The presence of unsolicited insights of social media is gradually making it a powerful ally to governments, policymakers, and organizations in detecting unexpected stakeholder concerns that can shape policy and infuence products (Kaplan and Haenlein 2010; Lee and Kwak 2012). Accordingly, this study uses social media discourse to examine public perception, drivers, and challenges of PA. Using a literature-driven approach, the study con- trasts extant literature with over 40 000 publicly available posts. The study explored users’ feelings on the topic of PA via sentiment and emotion analysis. Sentiment analysis revealed that users often expressed a positive stance on PA and emotion analysis discovered joy as the primary feeling expressed in PA discourse. Additionally, using a supervised machine learning approach that made use of human coders to hand-label posts, the most prevalent drivers were found to be on policy or labor discussions, while discussions on challenges were most likely on cost and complexity of available technology. The present study demonstrates that social media can uncover and clarify topics of public interest. The results substantiate those found in literature and provides an opportunity for theory-building that relies on public perception of PA. For example, SM discourse supports the results of prior researchers and have a contradictory view to that of popular technology leaders on the future of jobs in agriculture. Given such promising results, the study clarifes the public stance on previous research and streamlines direction for future studies. This study also provides areas of focus for technology providers in agriculture and serves as a guide for further development. For instance, the prevalence of discourse on complex software and expensive hardware should be addressed both as an academic and commercial issue as it could be the key to increasing employment in agriculture. Further, this study concludes that a united front is required to promote efective governance and policies, especially those that address data security and privacy. In efect, a streamlined policy on data governance in agriculture could mitigate farmer suspicion regarding the loss of competitive advantage as a result of increased data-enabled technology use.

Appendix

See Tables 5 and 6.

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Precision Agriculture fnancing NDCs in Ghana is the lack of private sector sector of private fnancing NDCs in Ghana is the lack climate-smart that promotes agriculture.#Clim investment ateFinance4Ag but need connectivity, trust and governance but need connectivity, to be realised to cost, time, complex technology, ROI, service and support. ROI, technology, time, complex cost, #agdata16 potential. It’s your data and you own it as the farmer own data and you your It’s potential. support assisting the existing GPS driven tractors. Satel - tractors. GPS driven support the existing assisting spectrum of of infrared stress images detect lites take to the it to can and transmit GPS human eyes before crops tractors to address the interlinked challenges of food security and of food challenges the address to interlinked change #climatechange crop and animal health—and it allows for best use of time best for and animal health—andcrop it allows for perspectives Smart farmers. opens new farming by #eaAgriFood too! farmers #FutureofCAP young Forum, #Rwanda to promote climate-smart promote to #agricultureForum, #Rwanda #livelihoods—CUTS and protect Ghana GH Case Study: One of the gaps identifed in One of the gaps Ghana GH Case Study: Digital agriculture: rapid feedback a game-changer for ag, for a game-changer Digital agriculture: feedback rapid Training is vital if the agriculture benefts of precision Training are 5 barriers practices: farming precision implementing to Data collected in precision farming has great value and Data has great farming value collected in precision Precision Agriculture works best when you can have data can have when you best AgriculturePrecision works Climate-smart agriculture (CSA) is an integrative approach Climate-smart approach is an integrative agriculture (CSA) Smart farming ofers great potential. Very promising for for promising Smart Very ofers great farming potential. Include key stakeholders in the National Trade Policy Policy Trade in the National stakeholders Include key Example tivity, energy, hardware, partner, startup, invest, business startup, partner, invest, hardware, energy, tivity, ability, trust ability, regulation drones, robotics, machine learning machine robotics, drones, security, waste security, labor, staf labor, bill subsidies infrastructure, digitization, equipment, efciency, connec - efciency, infrastructure, digitization, equipment, standards, traceability, confdence, convenience, account - confdence, convenience, traceability, standards, education, research, guide, training, education, research, cost, complex, expense, expensive, savings, ROI, support ROI, savings, expensive, expense, complex, cost, data, privacy, hack, open data, security, access, ownership, access, ownership, open data, security, hack, data, privacy, IoT, big data, AI, sensor, blockchain, embedded, nanotech, embedded, nanotech, blockchain, big data, AI, sensor, IoT, climate, water, pollution, ecosystem, environment, food food environment, pollution, ecosystem, climate, water, employ, automate, replace, opportunity, human, robot, human, robot, opportunity, replace, automate, employ, government, policy, legislation, support, tax, framework, legislation, support, tax, framework, policy, government, Sample keywords Sample - Codebook for categorizing posts Codebook for ment opportunities Infrastructure and investment Trust and accountabilityTrust Education and training Cost and complexity of Technologies and complexity Cost Data ownership, privacy, security, and transparency security, privacy, Data ownership, Technology as an enabler Technology Global climate and environmental change Global climate and environmental Automation efects on human capital and employ Automation Governance, policies, and trade opportunities policies, and trade Governance, 5 Table Category

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