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A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda

While there is a lot of literature from a natural or technical sciences perspective on different forms of digitalization in agriculture (big data, internet of things, augmented reality, robotics, sensors, 3D printing, system integration, ubiquitous
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  Contents lists available at ScienceDirect NJAS - Wageningen Journal of Life Sciences  journal homepage: Review A review of social science on digital agriculture, smart farming andagriculture 4.0: New contributions and a future research agenda Laurens Klerkx a, *, Emma Jakku b , Pierre Labarthe c a  Knowledge, Technology and Innovation Group, Wageningen University, The Netherlands b CSIRO Land and Water, Ecosciences Precinct Dutton Park, Queensland, Australia c  INRA, UMR AGIR, Toulouse, France A R T I C L E I N F O  Keywords: Robotic farmingPrecision agricultureDigitalizationDigital social scienceData scienceResponsible research and innovationAgricultural knowledge and innovation systems A B S T R A C T While there is a lot of literature from a natural or technical sciences perspective on di ff  erent forms of digitali-zation in agriculture (big data, internet of things, augmented reality, robotics, sensors, 3D printing, systemintegration, ubiquitous connectivity, arti 󿬁 cial intelligence, digital twins, and blockchain among others), socialscience researchers have recently started investigating di ff  erent aspects of digital agriculture in relation to farmproduction systems, value chains and food systems. This has led to a burgeoning but scattered social sciencebody of literature. There is hence lack of overview of how this  󿬁 eld of study is developing, and what areestablished, emerging, and new themes and topics. This is where this article aims to make a contribution, beyondintroducing this special issue which presents seventeen articles dealing with social, economic and institutionaldynamics of precision farming, digital agriculture, smart farming or agriculture 4.0. An exploratory literaturereview shows that  󿬁 ve thematic clusters of extant social science literature on digitalization in agriculture can beidenti 󿬁 ed: 1) Adoption, uses and adaptation of digital technologies on farm; 2) E ff  ects of digitalization on farmeridentity, farmer skills, and farm work; 3) Power, ownership, privacy and ethics in digitalizing agriculturalproduction systems and value chains; 4) Digitalization and agricultural knowledge and innovation systems(AKIS); and 5) Economics and management of digitalized agricultural production systems and value chains. Themain contributions of the special issue articles are mapped against these thematic clusters, revealing new in-sights on the link between digital agriculture and farm diversity, new economic, business and institutionalarrangements both on-farm, in the value chain and food system, and in the innovation system, and emergingways to ethically govern digital agriculture. Emerging lines of social science enquiry within these thematicclusters are identi 󿬁 ed and new lines are suggested to create a future research agenda on digital agriculture, smartfarming and agriculture 4.0. Also, four potential new thematic social science clusters are also identi 󿬁 ed, which sofar seem weakly developed: 1) Digital agriculture socio-cyber-physical-ecological systems conceptualizations; 2)Digital agriculture policy processes; 3) Digitally enabled agricultural transition pathways; and 4) Global geo-graphy of digital agriculture development. This future research agenda provides ample scope for future inter-disciplinary and transdisciplinary science on precision farming, digital agriculture, smart farming and agri-culture 4.0. 1. Introduction 1.1. Digitalization as a transformative force in agricultural production systems, value chains and food systems Digitalization, the socio-technical process of applying digital in-novations, is an increasingly ubiquitous trend. Digitalization comprisesphenomena and technologies such as big data, internet of things (IoT),augmented reality, robotics, sensors, 3D printing, system integration,ubiquitous connectivity, arti 󿬁 cial intelligence, machine learning, di-gital twins, and blockchain among others (Alm et al., 2016; Smith, 2018; Tilson et al., 2010). Digitalization is expected to radically transform everyday life (Yoo, 2010) and productive processes in agri-culture and associated food,  󿬁 bre and bioenergy supply chains and 16 October 2019; Received in revised form 28 October 2019; Accepted 28 October 2019 ⁎ Corresponding author.  E-mail address: (L. Klerkx). NJAS - Wageningen Journal of Life Sciences 90–91 (2019) 100315Available online 19 November 20191573-5214/ © 2019 The Authors. Published by Elsevier B.V. on behalf of Royal Netherlands Society for Agricultural Sciences. This is an open access article under the CC BY license (    systems (Poppe et al., 2013; Smith, 2018) and initial signs of trans- formation are already visible (Di Silvestre et al., 2018; Leviäkangas, 2016; Rotz et al., 2019a). 1 In the agricultural sector, several conceptshave emerged to express di ff  erent forms of digitalization in agriculturalproduction systems, value chains and more broadly food systems. Theseinclude  Smart Farming   (Blok and Gremmen, 2018; Wolfert et al., 2017),  Precision Agriculture  or  Precision Farming   (Wolf and Buttel, 1996;Eastwood et al., 2017b)  , Decision Agriculture  (Leonard et al., 2017)  , Digital Agriculture  (Keogh and Henry, 2016; Shepherd et al., 2018),  Agriculture 4.0  (Rose and Chilvers, 2018) or what is referred to inFrench as  Agriculture Numé r ique  (i.e.  Numerical Agriculture  - Bellon-Maurel and Huyghe, 2016). 2 Regardless of the exact term used, digi-talization implies that management tasks on-farm and o ff  -farm (in thebroader value chain and food system) focus on di ff  erent sorts of data(on location, weather, behaviour, phytosanitary status, consumption,energy use, prices and economic information, etc.), using sensors, ma-chines, drones, and satellites to monitor animals, soil, water, plants andhumans. The data obtained is used to interpret the past and predict thefuture, to make more timely or accurate decisions, through constantmonitoring or speci 󿬁 c big data science enquiries (Eastwood et al.,2017a; Janssen et al., 2017; Wolfert et al., 2017). Digitalization in agriculture is thus expected to provide technicaloptimization of agricultural production systems, value chains and foodsystems. Furthermore, it has been argued that it may help address so-cietal concerns around farming, including provenance and traceabilityof food (Dawkins, 2017), animal welfare in livestock industries (Yeates, 2017) and the environmental impact of di ff  erent farming practices(Balafoutis et al., 2017; Busse et al., 2015). Digitalization is also ex- pected to enhance knowledge exchange and learning, using ubiquitousdata (Baumüller, 2017; Daum et al., 2018; Eichler Inwood and Dale, 2019) and improve monitoring of crises and controversies in agri-cultural chains and sectors (Stevens et al., 2016). The uptake inter-nationally of digital technologies in the past two decades has been mostprevalent in agricultural sectors such as cropping and viticulturethrough precision farming technologies (Bramley, 2009), and to a lesserextent in animal-based farming (Borchers and Bewley, 2015; Eastwood et al., 2017a), and there are high expectations as regards its furtherdi ff  usion and transformative potential (Rose and Chilvers, 2018;Shepherd et al., 2018).The scienti 󿬁 c literature on digital agriculture has primarily focusedon the technical aspects of applying these technologies for improvingagricultural practices and productivity (Rutten et al., 2013; Wathes et al., 2008), as well as improving post-farmgate processes, such aspostharvest quality monitoring in logistic process and real-time trace-ability (Wolfert et al., 2017). By now, there is a large body of pre-dominantly natural and design science oriented literature on (potential)applications of digital technologies in agriculture. This is evidenced byan increasing number of review articles on topics such as precisionfarming, big data analysis, drones, arti 󿬁 cial intelligence and robotics,3D printing, arti 󿬁 cial intelligence, IoT, and the transformative potentialof these digital technologies for agricultural production systems, valuechains and food systems (de Amorim et al., 2019; Dick et al., 2019; El Bilali and Allahyari, 2018a; Hunt and Daughtry, 2018; Kamilaris et al., 2017; Mogili and Deepak, 2018; Patrício and Rieder, 2018; Portanguen et al., 2019; Shamshiri et al., 2018; Skvortsov et al., 2018; Smith, 2018; Verdouw et al., 2013, 2016a,b; Voon et al., 2019; Weersink et al., 2018; Zhang and Wei, 2017; Zhao et al., 2019). 1.2. The importance of interdisciplinary social science on digital agriculture:aims of the special issue and this introductory review article The body of social science literature on social, economic, and in-stitutional approaches to investigating digital agriculture has also beengrowing in recent years. It has for example looked at management as-pects of the digitalization of agriculture, the social dimensions of theinnovation processes surrounding the digitalization of agriculture, butalso critically scrutinizing its impact on people, institutions, animalsand ecosystems. Whereas some of the early publications date from morethan two decades ago (Wolf et al., 2001; Wolf and Buttel, 1996; Wolf  and Wood, 1997), given the technological advances and the perva-siveness of digital technologies in all realms of society (Scholz et al.,2018), the last 5 years have seen an accelerated growth in social sciencepublications on digital agriculture (as will also become clear in section2). The topic of social, economic and institutional aspects of digitalagriculture has also received increasing dedicated attention at scienti 󿬁 cconferences, such as the 2018 International Farming Systems Associa-tion symposium, the 2019 European Society for Rural Sociology con-ference, and the upcoming 2020 International Rural Sociology Asso-ciation conference, with numerous speci 󿬁 c session, themes andworkshops. Large science and innovation programmes also focus ondigital agriculture and the social, economic and institutional aspects.These include the European Horizon 2020 project  ‘ Internet of Farm &Food ’  (IoF2020), the Digiscape Future Science Platform from CSIRO inAustralia, the #DigitAg programme in France, the NZBIDA project fromAgResearch in New Zealand, the DESIRA, FAIRshare and Smart-AKISprojects and thematic networks in Europe, the Canada Digital Agri-Foodprogramme and the Cornell Open Ag initiative in the United States.There is also a growing interest in the topic of digital agriculturewithin policy circles, including the socio-economic elements of digita-lization, and this has resulted in several policymaker and practitioneroriented publications. For instance, the Standing Committee onAgricultural Research from the EU, through its Agricultural Knowledgeand Innovation Systems Strategic Workgroup, has a speci 󿬁 c focus onSmart Farming/Digital Agriculture (Poppe et al., 2013; EU SCAR AKIS, 2019) and reviews commissioned by the EU have emerged on the topic(Soma et al., 2019). The World Bank has published a sourcebook and afuture outlook (World Bank, 2019; World Bank, 2017), and the FAO has recently published a status report (Trendov et al., 2019), all of thempresenting several experiences with and models of digital agriculture.The Australian Farm Institute has organized three policy orientedconferences on  ‘ Digital Disruption ’  in recent years (2016 – 2018), whilethe OECD also organized a conference in 2018 on the topic and pub-lished a policy-oriented publication (Jouanjean, 2019).Considering the growing attention to digital agriculture in bothnatural and social sciences, as well as policy discourse, the authors of this introductory review article, as guest editors of this special issue,thought it would be timely to bring together global experiences of socialand institutional responses to digital agriculture. The idea was that withdigital agriculture applications becoming more established, it was agood moment to bring together a collection of conceptual and empiricalsocial science articles on this topic. Therefore, the special issue ori-ginally aimed to answer the following question as formulated in the callfor articlespublished in 2018:  what recon  󿬁  gurations of practices and in- stitutions are emerging to embed and enact digital agriculture technologiesand counteract possible negative consequences?  The call indeed yieldedsome articles showing recon 󿬁 gurations of practices and institutions,showing for example new digital agriculture business models, newadvisory practices, as well as showing practical and institutional di ffi -culties and challenges in dealing with digital agriculture. Additionally,the call has also yielded conceptual re 󿬂 ections on ethics, digital socialsystem concepts, and digital innovation concepts (see section 3). Giventhe recent surge of social science literature on digital agriculture noted 1 At the same time, it should be recognised that digitalization in agriculturehas been going for decades already, expressed for example through work ondecision support systems (Leeuwis, 1993, 1999. McCown, 2002) and also pre- cision agriculture has a long trajectory already (Wolf and Buttel, 1996; Wolf  and Wood, 1997). 2 While being cognizant of the diversity of terms employed in the literature,and also by the articles in the special issue, for the sake of clarity this articlehenceforth mainly employs the term  ‘ digital agriculture ’ .  L. Klerkx, et al.  NJAS - Wageningen Journal of Life Sciences 90–91 (2019) 100315 2  above, of which many articles were actually published during the de-velopment of this special issue, the contributions of the 17 articles inthis special issue do not fall into a vacuum but add to a rapidly growingbody of social science literature.However, while there has been growing interest in digital agri-culture from di ff  erent social science disciplines (such as sociology,geography, innovation studies and economics), as well as humanitiesdisciplines (such as ethics, law and philosophy) 3 , the extant social sci-ence literature on digital agriculture is rather scattered. Despite theexistence of some review articles on particular issues, such as the po-litical economy of digital agriculture (Rotz et al., 2019a) and perspec-tive articles on ethics (Carbonell, 2016), most review articles focus ontechnical issues or aim to provide an overview of the state of the artwithin a certain sub 󿬁 eld (Aker, 2011; Banhazi et al., 2012; Protopop and Shanoyan, 2016; Baumüller, 2017; Eichler Inwood and Dale, 2019; Kamilaris et al., 2017; Mogili and Deepak, 2018; Patrício and Rieder, 2018; Salemink et al., 2017; Verdouw et al., 2013; Wolfert et al., 2017; Zhao et al., 2019).This introductory review article therefore aims to provide an over-view and thematic clustering of di ff  erent, yet related, disciplines of social science literature on digital agriculture, show what the specialissue articles add to this body of work and provide an agenda for futureresearch. The three speci 󿬁 c questions it seeks to answer are: 1) what arethe main thematic clusters of social science literature on digitalizationin agriculture, based on an explorative review; 2) what are the maininsights from the articles in this special issue and how do they connectto these thematic clusters and/or open new lines of enquiry?; and 3)what are some possible future questions still to be explored by socialsciences in this  󿬁 eld?In answering these questions, this introductory review article andthe special issue as a whole demonstrate an interdisciplinary perspec-tive at two levels. The  󿬁 rst level of interdisciplinarity is between socialscience disciplines. The special issue brings together contributions fromsociology, science and technology studies, economics, design thinkingand policy studies, showing the diversity and complementarity of an-gles on the topic of digital agriculture. These angles highlight both thepositive and negative e ff  ects that digitalisation might have on a sus-tainable development of agriculture, food systems and rural areas. Italso stresses the need to support the re 󿬂 exivity of actors (farmers, ad-visors, policy makers, researchers) contributing to the development of digital agriculture.The critical social science perspective on digitalization and digitalagriculture taken in this special issue has important implications for asecond level of interdisciplinarity, between social sciences and natural,technical or life sciences, which is a key aim of NJAS-WageningenJournal of Life Sciences. Outlining a future research agenda and newquestions for social sciences on digitalisation could help researchersfrom other disciplines shed a new light on the direction and conditionsthey take into account when developing, testing, implementing andscaling new digital technologies. The article proceeds as follows. Insection 2 we outline  󿬁 ve major current thematic clusters in the socialscience literature, and in section 3 we map the special issue articlecontributions against these thematic clusters. Then, in section 4 weraise several emerging questions in the  󿬁 ve existing thematic clusters,but also present four potential thematic clusters with sets of newquestions. Section 5 presents some concluding remarks and a call forboth social and natural sciences to engage with this future researchagenda on social, economic, legal, organizational and ethical aspects of digital agriculture through interdisciplinary and transdisciplinary ap-proaches. 2. Major current thematic clusters in the social science literatureon digitalization in agriculture The overview of thematic clusters of social science literature pre-sented in this section is based on an exploratory review 4 , achieved bysearching with keywords such as  ‘ digital agriculture ’ ,  ‘ smart farming ’  inthe comprehensive scienti 󿬁 c database Scopus, with a focus on socialscience or interdisciplinary journals focused on agriculture (such as theJournal of Rural Studies, the Journal of Peasant Studies, SociologiaRuralis, Agricultural Systems, NJAS-Wageningen Journal of Life Sci-ences, Land Use Policy, and the Journal of Agricultural Education andExtension). Furthermore, snowball methods were employed, such asusing references in articles found, or screening articles citing pioneerwork on digitalization. This led to over 100 social science articles ondigital agriculture, and allowed for the identi 󿬁 cation of   󿬁 ve majorthematic clusters of social science literature related to digitalization inagriculture, some established and others emerging. The di ff  erent the-matic clusters draw on di ff  erent social science disciplines (such as so-ciology, geography, economics, communication science, managementscience and innovation science) and humanities (philosophy, ethics),hence supporting interdisciplinary debates rather than a juxtapositionof disciplinary standpoints on digitalization. Table 1 provides anoverview of articles reviewed per thematic cluster, social science dis-ciplines, the theoretical and methodological perspectives and what ar-ticles in the special issue pertain to the di ff  erent thematic clusters(detailed in section 3). Note that we focus here on agriculture, and noton how digitalization a ff  ects rural areas, which is a broader topic goingbeyond agriculture alone (see e.g. Roberts et al., 2017; Salemink et al., 2017).  2.1. Adoption, uses and adaptation of digital technologies on farm This  󿬁 rst thematic cluster is well established, with one line of en-quiry focused on di ff  erent aspects of precision technology adoption onfarm 5 , examining both economic and behavioural aspects. This litera-ture concentrates on individual adoption determinants (Barnes et al.,2019; Hansen, 2015; Jensen et al., 2012; Kernecker et al., 2019; Leonard et al., 2017; Tey and Brindal, 2012), as well as extension and communicative interventions to stimulate adoption (Kutter et al.,2011). Another line of enquiry examines precision agriculture use onfarm and how it a ff  ects farming practices (Fountas et al., 2005; Hansen, 2015; Hay and Pearce, 2014) and post-adoption processes of adaptation (Higgins et al., 2017; Schewe and Stuart, 2015), through concepts such as  ‘ tinkering ’  and  ‘ assemblages ’ . The latter topic has also been analysedfrom a beyond-farm level perspective, looking at the broader networksand innovation systems in which technology is shaped and where co-evolution between the technology and broader social and institutionalenvironments takes place (Eastwood et al., 2017, 2012). This clusterbuilds on a variety of methods, ranging from modelling approaches of the costs and bene 󿬁 ts of precision farming (Schimmelpfennig and Ebel,2016), quantitative or econometric approaches testing the e ff  ects of di ff  erent variables on adoption (such as farm size and specialisation,farmers ’  age, education, etc., see Annosi et al., 2019; Barnes et al., 2019; Lowenberg-DeBoer and Erickson, 2019), to more qualitative work, highlighting the situation of both adopters and non-adopters, and 3 While we recognize that humanities comprise di ff  erent disciplines thanthose considered part of the social sciences, in the remainder of the paper wewill use the term social science as an umbrella term. So when we talk aboutsocial science, we sometimes also refer to humanities in this article. 4 This review was not a systematic review, as that would go beyond the scopeof this introductory article. Nonetheless the authors feel the search results werequite comprehensive and allow for the clustering presented. 5 While precision agriculture is a form of digital agriculture, and is alsoconnected with broader value chain structures (e.g. Carolan, 2018b), somecommentators argue that concepts such as Smart Farming and Agriculture 4.0are  ‘ larger ’  concepts, since as Smart Farming includes digitalization of supplychains and food systems as a whole and Agriculture 4.0 may also compriseother technologies such as gene editing (Wolfert et al., 2017; Rose and Chilvers, 2018).  L. Klerkx, et al.  NJAS - Wageningen Journal of Life Sciences 90–91 (2019) 100315 3  accounting for less measurable aspects, such as material contingenciesand cultural dimensions of knowledge (Higgins et al., 2017). It shouldalso be noted that while most research in industrial contexts focuses onthe adoption of precision farming technologies, research in Africa ra-ther focuses on the adoption (or non-adoption) of market informationsystems (Wyche and Stein 󿬁 eld, 2016). The literature on the Africancontext looks at agriculture speci 󿬁 c decision support tools as well as therole of generic technologies, such as cell phones, in access to informa-tion on input and commodities prices (Baumüller, 2017).  2.2. E   ff  ects of digitalization on farmer identity, farmer skills, and farmwork This thematic cluster, which is also well established, focuses on howdigital technologies impact on the method of farming, demanding dif-ferent knowledge, skills and labour management among farmers. Onestrand of enquiry is rooted in systems design and focuses on the prac-tical issues of human-robot interaction in farming, such as ergonomicsand health and safety, as a review article by Vasconez et al. (2019)shows. Another strand of research within the  󿬁 eld of rural sociologylooks at broader socio-cultural implications, drawing on a range of theorists (e.g. Foucault, Latour, Durkheim, Giddens) and perspectives,such as political economy and assemblage theory. Digitalization canhave major impacts on the cultural fabric of rural areas and farmeridentities as it a ff  ects what it means to be a farmer (Burton et al., 2012;Carolan, 2017b). Digitalization may change the culture of farming from ‘ hands-on ’  and experience driven management to a data-driven ap-proach (Butler and Holloway, 2016; Carolan, 2017b, 2019a; Eastwood et al., 2012) and may  ‘ discipline ’  farmers ’  work routines in certain ways(Carolan, 2019), conditioned by  ‘ algorithmic rationality ’  (Miles, 2019).As a consequence, the compatibility of digitalization with approachessuch as agro-ecology is a matter of debate (Plumecocq et al., 2018; Van Hulst et al., 2019), as it has been argued that agro-ecology wouldspeci 󿬁 cally require hands-on farming as opposed to digitally-mediatedfarming.Questions have also been raised about the e ff  ect of digitalization onfarmers ’  autonomy, including concerns about farmers becoming  ‘ datalabourers ’  (Rotz et al., 2019b). Digitalization has also been found toa ff  ect gendered identities on farms (Bear and Holloway, 2015; Hay and Pearce, 2014). Furthermore, technology aimed at automating tasks andincreasing e ffi ciency may deskill or displace farmers and farm workersand exclude or discriminate against those not digitally literate. Thismay have negative e ff  ects on demand for rural labour and hence a ff  ectmarginalized groups such as migrants, in a context of growing separa-tion between labour and capital in agriculture (Carolan, 2019; Rotz et al., 2019b; Smith, 2018). However, other authors argue that digital technologies may also be merged into existing practices to createcombinations of   ‘ digital ’  and  ‘ analogue ’  skills (Burton and Riley, 2018), Table 1 Overview of thematic clusters in terms of social science disciplines, theoretical and methodological perspectives, and how they link to special issue articles. Thematic cluster Number of reviewed articlesin this thematic cluster(some are present in severalclusters)Principal social science disciplinesinvolvedExamples of theoretical andmethodological perspectives used inthis thematic clusterArticles in the specialissue pertaining to thisthematic clusterAdoption, uses and adaptation of digitaltechnologies on farm16 Economics, sociology, innovationstudies, science and technologystudiesAdoption and di ff  usion theoryBehavioral psychologyPractice theoryAssemblage theoryCost and bene 󿬁 t modellingEconometricsEvolutionary economicsInnovation systemsJanc et al., 2019Knierim et al, 2019E ff  ects of digitalization on farmeridentity, farmer skills, and farmwork16 Sociology, social geography,anthropologyPolitical economyPractice and identity theoryStudies of discourse, power, politicsand social transformation (e.g.Foucault, Bourdieu, Durkheim,Giddens)Actor-network theoryAssemblage theoryGender studiesEthnographyFarming stylesCultural scriptsVik et al., 2019Lioutas et al., 2019Power, ownership, privacy and ethics indigitalizing agricultural productionsystems and value chains28 Sociology, political science,philosophy and ethics, science andtechnology studiesPolitical economyInstitutional economicsAnimal ethicsHuman ethicsResponsible Research and InnovationActivity theoryVan der Burg et al.,2019Lioutas et al., 2019Jakku et al., 2019Bronson, 2019Wiseman et al., 2019Regan, 2019Digitalization and agriculturalknowledge and innovation systems27 Innovation studies, science andtechnology studies,communication science,economicsKnowledge and Innovation systemsSocial media analysisLearning theoriesEvolutionary economicsSocio-technical transitionsFielke et al., 2019Ingram and Gaskell,2019Relf-Eckstein et al.,2019Rijswijk et al., 2019Eastwood et al., 2019Ayre et al., 2019Economics and management of digitalized agricultural productionsystems and value chains21 Economics, management science,sociologyValue chain theoriesBusiness model theoriesRisk analysisInstitutional economicsService economicsPhillips et al., 2019Rojo Gimeno et al., 2019  L. Klerkx, et al.  NJAS - Wageningen Journal of Life Sciences 90–91 (2019) 100315 4  or give rise to a new sort of   ‘ responsible professionalism ’  (Blok, 2018).  2.3. Power, ownership, privacy and ethics in digitalizing agricultural production systems and value chains This established cluster of work applies critical social science per-spectives on digitalization in agriculture, focusing on the politicaleconomies and political ecologies of digital agriculture. Key issues inthis thematic cluster include issues of power, data ownership, inclusionand exclusion, privacy, and how to deal with these issues ethically. Onestrand of enquiry uses the lens of corporate structures in relation toproduction systems and supply chains, often with political economy orscience and technology studies perspectives. This literature examineshow digitalization changes or reproduces the rules, institutions andbalances of power governing these systems, how that a ff  ects di ff  erentactors and what responses or resistances emerge and how ethical issuessuch as those around privacy and data ownership emerge and are ad-dressed (Sykuta, 2016; Bronson, 2018; Carbonell, 2016; Carolan, 2017a, 2018a,b,c, 2019; Eastwood et al., 2017a; Fleming et al., 2018; Fraser, 2019a, b; Freidberg, 2019; Rose and Chilvers, 2018; Rotz et al., 2019a; Schuster, 2017; Miles, 2019). This research includes critiques of  the lack of policy interventions tackling the digital divides produced byrapid, unregulated technological change and the power imbalances thatcould constrain the integration of societal issues (Bronson andKnezevic, 2019). Other research identi 󿬁 es how risks such as cyber-at-tacks may destabilize precision agricultural systems and digitalizedfood systems (Barreto and Amaral, 2018; Trendov et al., 2019; West, 2018).Besides digital agriculture having implications for humans, animalsare also a ff  ected by digital agriculture. This happens for example indairy farming, where this is operationalized through approaches such asrobotic milking systems (Driessen and Heutinck, 2015) and use of technologies to replace animal husbandry tasks (Butler and Holloway,2016). Robotic milking adoption has been shown to involve a variedrange of factors, and therefore equally varied outcomes for animals,people, and the environment (Schewe and Stuart, 2015). This has givenrise to philosophical and ethical perspectives, in which ethical chal-lenges a ff  ecting animal autonomy and human-animal relationships of farms have been analysed (Bear and Holloway, 2019; Driessen and Heutinck, 2015; Bos and Munnichs, 2016).  2.4. Digitalization and agricultural knowledge and innovation systems Digitalization has also been observed to be a driving force of theevolution of agricultural knowledge and innovation systems (AKIS). Inthis thematic cluster, which has emerged recently but is increasinglybecoming established, di ff  erent lines of enquiry can be discerned witheither a macro, meso or micro perspective on knowledge and innova-tion systems. From a macro perspective, some research that uses in-novation systems perspectives looks at how innovation support struc-tures enable digitalization, but also change themselves under thein 󿬂 uence of digitalization, e.g. by incorporating big data analysis(Kamilaris et al., 2017). Some research also looks at how AKIS for di-gital agriculture are shaped through a diversity of existing and newactors in these systems: high-tech  󿬁 rms (e.g. drones or satellite manu-facturers, etc.), service industries, and multinationals producingfarming equipment, such as self-driving tractors and automated milkingmachines (Eastwood et al., 2017b). Given the ethical concerns raised incluster 3 (section 2.3), there is an emerging literature that explores howinnovation systems can apply principles of Responsible Research andInnovation (RRI) (Owen et al., 2012) to the digitalization of agriculturalproduction systems, value chains and food systems (Bronson, 2018;Eastwood et al., 2017a; Jirotka et al., 2017; Rose and Chilvers, 2018). This literature also explores the role that transdisciplinary science canplay in supporting integrative solutions that look at a combination of technological, ethical, social, economic and business challenges(Shepherd et al., 2018). At a meso perspective, some research, drawingon theories of learning and communication, looks at how networks of learning are formed to enable digital agriculture innovation (Eastwoodet al., 2012; Kelly et al., 2017; Van Der Vorst et al., 2015). For example, some studies examine how digital platforms and social media enablelocal and global information sharing and peer learning (Aker, 2011;Baumüller, 2016; Burton and Riley, 2018; Chowdhury and Hambly Odame, 2013; Jespersen et al., 2014; Kaushik et al., 2018; Kelly et al., 2017; Munthali et al., 2018). Other studies have also looked at how user generated data, through social media analysis and citizen science ap-proaches, feeds into real-time decision making and informs policy de-cisions (Cieslik et al., 2018; Leeuwis et al., 2018; Stevens et al., 2016). At the micro-level of knowledge systems, using theories of learning anduser centred-design, other research looks at the continuous processes of how digital decision support systems are better attuned to users(O ’ Donoghue et al., 2016; Antle et al., 2017; Rose et al., 2018; Driessen and Heutinck, 2015; Rose et al., 2016; Lindblom et al., 2017) and how advisors interact with farmers to connect  ‘ digital knowledge systems ’  to ‘ farmers knowledge systems ’  (Tsouvalis et al., 2000; Lundström and Lindblom, 2018; Bechtet, 2019).  2.5. Economics and management of digitalized agricultural production systems and value chains While there is some generic (i.e. non-agriculture speci 󿬁 c) literaturelooking at economic and business model aspects of digital technologiesand big data (see e.g. Koch and Windsperger, 2017; Teece, 2018; Teece and Linden, 2017), in agriculture the body of work with this focusseems to be more modest. There is some research re 󿬂 ecting on costs andbene 󿬁 ts of unmanned aircraft systems, for example (Hunt andDaughtry, 2018), or of other precision farming technologies(Schimmelpfennig and Ebel, 2016). Related to the literature on preci-sion technology adoption, one strand of enquiry in this emerging the-matic cluster looks at investment decisions (Rutten et al., 2018). Someresearch has tried to assess the e ff  ect of precision farming technologieson productivity in the agricultural sector. Lio and Liu (2006) for in-stance show a positive e ff  ect of these technologies, but also potentialdivergences and inequalities across countries. Some pieces re 󿬂 ect, be-yond the farm level, on the (potential) economic impacts of digitalizedsupply chains (Jouanjean, 2019; Smith, 2018), and big data services and analysis (Boehlje, 2016; Sykuta, 2016). Another important stream of research deals with the economic im-pact of digital technologies on markets, mostly using theoretical andmethodological approaches embedded in micro-economics, modellingand econometrics of the relation between demand, supply and patternsof use of information. In the context of developing countries, manystudies have assessed the impacts of market information systems tocompensate for asymmetries of information and enhance access tomarkets (David-Benz et al., 2017; Aker, 2011; Islam and Grönlund, 2010; Agyekumhene et al., 2018). In the context of industrialised agriculture, there are discussions about actors developing informationsystems to support farmers in risk management, be they climatic or 󿬁 nancial risks (Fraisse et al., 2006). The business models associatedwith these services are often related to new forms of insurance forfarmers, such as index-based climate insurance systems. Nevertheless,empirical research about business models of digital agriculture remainsrare, and typologies are often limited to new direct marketing solutionsbetween farmers and consumers (Andreopoulou et al., 2008). Con-nected to the issue of power, as mentioned in section 2.3, some studiestaking a political or institutional economy or value chain perspective,highlight potential downsides of vertically integrated systems and newbusiness models. In such models, multinational corporations o ff  er large ‘ digital package deals ’  to farmers (Bronson, 2018; Bronson and Knezevic, 2016; Carolan, 2017b, 2018b). These package deals tend to maintain balances of power to the bene 󿬁 t of models of agriculturebased on the intensive use of chemical input, as hypothesised by Wolf   L. Klerkx, et al.  NJAS - Wageningen Journal of Life Sciences 90–91 (2019) 100315 5
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