Next Article in Journal
Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing
Previous Article in Journal
Effects of Ventilation Fans and Type of Partitions on the Airflow Speeds of Animal Occupied Zone and Physiological Parameters of Dairy Pre-Weaned Calves Housed Individually in a Barn
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 13
Issue 5
10.3390/agriculture13051003
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

How to Successfully Orchestrate Content for Digital Agriecosystems

by
Maximilian Treiber
*,†,
Theresa Theunissen
*,†,
Simon Grebner
,
Jan Witting
and
Heinz Bernhardt
Agricultural Systems Engineering, Technical University of Munich, Duernast 10, 85354 Freising, Germany
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2023, 13(5), 1003; https://doi.org/10.3390/agriculture13051003
Submission received: 6 April 2023 / Revised: 20 April 2023 / Accepted: 29 April 2023 / Published: 1 May 2023
(This article belongs to the Section Digital Agriculture)
Browse Figures
Review Reports Versions Notes

Abstract

:
Since the 2000s, digital ecosystems have been affecting markets—Facebook and Uber being prominent examples. Looking at the agrisector, however, there is not yet a winner-takes-all solution in place. Instead, numerous digital agriplatforms have emerged, many of which have already failed. In the context of this study, it was revealed that reasons for such failures can be manifold, with one key challenge being the orchestration of platform content. Because, however, publicly available knowledge on this regard is limited, we decided to introduce a methodology for the evaluation of digital agriecosystem services, enabling providers to optimize their existing offering and to prioritize new services prior to implementation. By deploying our methodology to digital agriecosystems with two different application focuses (DairyChainEnergy—data agriecosystem on energy management for dairy farmers, and NEVONEX—IoT agriecosystem comprising digital services for agrimachinery), its applicability was proven. Providers of digital agriecosystems will benefit from applying this new methodology because they receive a structured decision-making process, which takes the most relevant success criteria (e.g., customer benefit, technical feasibility, and resilience) into account. Hence, a resulting prioritization of digital agriservices will guide providers in making the right implementation choices in order to successfully generate network effects on their digital agriecosystems.

1. Introduction

Digital ecosystems—as defined in ref. [1], e.g., Google, Facebook, Amazon, Airbnb, or Uber—have already proven their potential for changing or even disrupting existing markets. Between 2015 and 2020, publicly traded platforms increased in number from fifty to over one hundred fifty platforms, with five out of the ten most successful digital companies on the Fortune’s Digital 100 being listed as digital ecosystem providers [2]. Their core added value lies in collaboration, networking, and openness, as well as in the provision of interlinked services. Multiple markets are already substantially dominated by such digital ecosystems—a phenomenon that is known as the “winner-takes-all effect”—advantaging solutions with a certain market-share threshold. Hence, digital ecosystem markets tend to sooner or later turn into either mono- or oligopolies [1]. In the agricultural (agri-) market, there are still no such dominating solutions—neither across nor within market segments (e.g., animal health monitoring, trading of agrigoods, farm management, etc. [3]). Instead, there is a rising number of digital farming solutions, such as embedded systems (e.g., sensors) for data gathering, mobile solutions (e.g., tablets) for on-site system accessibility, or digital platforms (e.g., farm management information systems (FMIS)) enabling the collaboration of multiple stakeholders [4]. Digital agriecosystems, on the other hand, orchestrate one or multiple digital platforms comprising a variety of interlinked digital agriservices that are provided, supported, and consumed by several stakeholders [1,5].
In the literature of this research field, there is still no common agreement on nomenclature; however, there is a noticeable differentiation between two types of digital agriecosystems: there are digital ecosystems with integrated Internet of Things (IoT) devices (IoT agriecosystems) [6] and those focusing on the processing and distribution of data (data agriecosystems) [5] (Figure 1). In this context, IoT solutions typically focus on one agrimarket segment, such as crop farming (e.g., NEVONEX—equipping agrimachinery with digital crop-farming services (DCFSs)) [7], energy management (e.g., CowEnergy—optimizing dairy barn on-farm power utilization) [8], or logistics (e.g., SISTABENE for tracking and tracing of agrigoods) [6]. This is because the architecture of an IoT agriecosystem’s physical and connectivity layers must be tailor-manufactured by considering the market segment’s specific requirements and framework conditions. For example, the NEVONEX IoT ecosystem operates based on electronic control units (ECUs) for agricultural machinery [9], whereas the use of CowEnergy requires implementing interfaces into the barn’s most relevant technical equipment [8]. This is because digital services of IoT agriecosystems are used to automate and optimize the execution of on-site processes (e.g., optimized control of a tractor’s tire-pressure control system [10] or real-time prioritization of electricity consumers in the barn [8]). In contrast, data agriecosystems such as Agri-Gaia, 365 FarmNet, or Xarvio serve stakeholders along agrisupply chains with digital services that are mostly restricted to the cloud layer (e.g., artificial intelligence (AI) data analytics solutions) but not necessarily limited to only one market segment [5,11,12]. Due to the dependency on actors or systems supplying data to this type of digital ecosystem, the maturity of data ecosystems is, however, comparably low [13]. Hence, the chances for scaling a data agriecosystem are much higher if there are already IoT agriecosystems in place to build upon. This is, for example, observable in livestock farming, wherein the market opportunity for providing digital energy-management services (DEMSs), e.g., in the context of the DairyChainEnergy project, is founded in existing IoT solutions such as CowEnergy [14].
Overall, digital agriecosystems—both IoT and data agriecosystems—can bring major benefits to the market (e.g., efficiency increase, cost reduction, and fraud prevention) and can enable the implementation of innovative business ideas (e.g., new channels for sales or collaboration) [15,16]. Beyond that, as observable in other industries, digital ecosystems can also improve the quality of digital services offered to the market [13]. However, despite this promising added value, a series of digital agriecosystems and platforms, including NEVONEX, Agrando, and Granular, recently announced their failures (e.g., due to low adoption or strategic reorientation) [17,18,19]. Because, on the other hand, success stories are rarely found and companies (such as Microsoft or Amazon Web Services) are still eager to conquer the market with new digital agriecosystems [20], our study aims to elaborate on ways to contribute to the future success of digital agriecosystems. Therefore, content orchestration is identified as one of the missing key success factors in current research, and a novel methodology for orchestrating content is developed based on the identification of relevant success criteria for the implementation of digital agriecosystem services. Further, this created methodology is consecutively evaluated on two real-life examples of digital agriecosystems to prove its applicability in practice.

2. Materials and Methods

To achieve this paper’s research target, two methods have been applied as illustrated in Figure 2. First, with the help of a root-cause analysis (RCA) in the style of ref. [21], causalities behind failures of digital agriecosystems were investigated. In this regard, for step I.1 (data collection), learnings from the build-up and shutdown of NEVONEX, as well as publicly available information on failure causes of digital agriecosystems from [16,22,23,24,25], were leveraged as input for this RCA. Next, a causal factor chart was created to organize and visualize events, conditions, and occurrences that led to failures of digital agriecosystems (step I.2). After deriving related root causes for such failures (step I.3), which are outlined in Section 3.1, we were able to derive the recommendation (step II.4) for developing a new methodology that addresses avoidable, strategy-related failure causes, more specifically, a methodology on content orchestration for digital agriecosystems (Section 3.2). In the context of this study, this methodology was shaped, executed, and enhanced with the help of findings from the RCA and additional insights from the literature [26,27,28,29,30], following a ten-step approach from ref. [22]. For example, to prove its applicability, we tested our newly developed methodology (step II.7) with the help of the two digital agriecosystem projects: NEVONEX and DairyChainEnergy (Section 3.3).

3. Results

3.1. Causality behind Failure of Digital Agriecosystems

The following paragraph identifies well-known and addressable success factors for digital ecosystems from the literature, followed by a root-cause analysis of the NEVONEX ecosystem ramp down. Comparing the identified root causes from the RCA to well-known, addressable success factors yields blind spots in the existing quiver of available methods and identifies the reasons why digital agriecosystems fail, despite following current state-of-the-art knowledge during implementation.
In the state-of-the-art literature, success and failure factors of digital platforms are comprehensively reviewed across industries. For example, ref. [22] pointed out that it is important to coordinate platform sides and digital interfaces with the scope of the firm operating the platform. Furthermore, ref. [23] identified thirty success factors for digital platforms and clustered them into three domains: corporate value integration, platform value, and platform architecture. In contrast, Cusumano et al. focused on failure causes and showed that many digital platforms fail during their first years following market launch due to mispricing, lack of trust, poor competitor analysis, and/or late market entry [24]. When looking beyond that, to the agridomain, it becomes apparent that there are additional challenges to overcome for making a digital ecosystem successful. For example, the authors in ref. [16] elaborated on a series of agrispecific challenges for implementing IoT technology that were clustered into business issues (cost, business models, and lack of adequate knowledge), technical issues (interference, security and privacy, choice of technology, reliability, scalability, localization, and optimization of resources), and sectoral issues (regulatory challenges and interoperability). In a related vein, the researchers in [25] identified sector-specific influencing factors for startups in the agridomain to overcome critical mass. For example, they state that it is beneficial to leverage the curiosity of early adopters in the agrimarket to further develop and improve digital solutions, but it is also very challenging for providers to overcome local growth [25].
In addition to these findings from the literature, during the preparation of this study, the NEVONEX project was shut down [17] so that first-hand information on the failure causes of digital agriecosystems could be derived by carrying out an ex post RCA. In this analysis, we focused on failure causes that could have been addressed and hence avoided by the providers of DCFSs. For the NEVONEX ecosystem, four remaining experts from the project were invited to develop the RCA together with the author throughout collaborative online meetings. The data were collected and clustered in mind maps. This RCA yielded a list of twenty-six root causes, which were clustered into three categories: “operations related” (9), “technology risk” (7), and “content orchestration related” (10). Root causes resulting from day-to-day business (e.g., human-resource capacity for daily stakeholder interaction) were labeled as “operations related,” whereas technology-related challenges (e.g., high complexity of required technical infrastructure) were labeled as “technology risks”. In contrast, failure causes related to content orchestration came from disagreements of stakeholders on the choice of digital services and low transparency on how this content choice would affect the digital agriecosystem and its stakeholders (e.g., digital service providers) [31]. However, whereas in the context of the NEVONEX project there were already methods applied to overcome operational and technical challenges, a decision-making process to adequately manage the digital ecosystem’s content orchestration was missing.

3.2. Introducing a Three-Phase Content-Orchestration Methodology for Digital Agriecosystems

To address one of the main causes behind the failure of digital agriecosystems identified in the context of our RCA, we applied steps II.1 through II.6 from Figure 2 to develop a methodology for organizing the orchestration of content on both evolving and mature digital agriecosystems. Hence, the purpose of this new methodology lies in enabling providers to explore pros and cons of digital ecosystem services prior to actually implementing them by considering their effects on stakeholders and the digital agriecosystem as a whole. To do so, we logically ordered the required steps for fulfilling this purpose, from content ideation to prioritization, resulting in a three-phase methodology (Figure 3). First, an exploration of promising application fields (A1–A3) is carried out based on work from [26,27,28], yielding a longlist of digital services eligible for the scope of the respective digital agriecosystem. For this exploration, an early-on involvement of different stakeholder groups (including potential consumers of the agriecosystems, e.g., farmers, and business partners, such as installation providers in the context of focus-group workshops or comparable formats) is recommended, whereas the choice of participants is decisive to ensure a good understanding of the status quo (with regards to both the agrimarket and the capabilities of the respective agriecosystem) as well as a sufficient degree of creativity and innovation [27]. In doing this, inappropriate use cases (e.g., digital services that are not backed up by an adequate market demand) are already filtered out in the first phase of our methodology. Next, to prepare the evaluation of digital services from the ideation longlist, providers can select from a predefined set of assessment criteria (customer benefit, society impact, economic provider benefit, governance implications, technical feasibility, and resilience). As pointed out in [29,30,32], the first three of those criteria are majorly relevant for assessing the value of a digital service, whereas the latter three criteria predominantly evaluate the implementability of a digital service. For example, when assessing the resilience of a digital service, threats for cybercrime must be taken into consideration [32]. In this context, this step (B1) ensures that ecosystem-specific characteristics can be taken into consideration (e.g., an economical assessment of digital services can be neglected when it comes to nonprofit digital farming solutions such as those from AgGateway that, for example, aim to establish an industry standard for processing data from precision agriculture [33,34]). Hence, if a criterion is regarded as not being relevant for the respective digital agriecosystem, it can be weighted with zero, i.e., eliminated from the assessment. In addition, there is a chance to determine a critical score per evaluation criterion that must be met by a digital service in order to remain in consideration (B2). In doing this, digital services that are not able to meet the minimum requirements of the respective digital agriecosystem will be filtered out. Next, to actually conduct the assessment per digital service and evaluation criterion, a mix of methods (e.g., exploratory expert interview, quantitative survey, tangible ecosystem design, and literature review) is applied to consider all criteria for evaluation. As the output from step B3, each digital service is assigned with an evaluation score from 0 to 5 on a Likert scale [35], which is used later on to aggregate the findings from the analysis and to create a ranking among the digital services in scope (C1). Hence, a weighted average of all six criteria assessments is to be calculated in order to form one assessment score per digital service in order to easily compare digital services with each other. Relevant stakeholders (especially service asset providers and brokers) should be aligned on the digital service ranking (C2) in order to ensure a future strategy fit (e.g., to make sure that the platform infrastructure can meet the technical requirements of newly planned digital services).

3.3. Testing our Methodology: NEVONEX and DairyChainEnergy

To validate its applicability, we tested our methodology with the two digital farming solutions, NEVONEX and DairyChainEnergy, following steps II.7 through II.10 from Figure 2. The scope for this testing was determined as follows: In the case of DairyChainEnergy, which is, so far, still in its conception stage, digital services were taken from [14], wherein the customer relevance of seven DEMSs was already analyzed in detail. For NEVONEX, in contrast, the use case exploration was carried out in a threefold approach: ideas for the DCFSs were received from (1) a review of functionalities on farm equipment (including those enabled by sensors and actuators), (2) a process-focused analysis of data streams of agrimachinery, and (3) feature ideation workshops conducted in the context of the NEVONEX project in the period from 2019 to 2022. As the result of this process, fifty-two use cases were received, of which ten DCFSs were determined to be within the scope of this study (Table 1).
For both NEVONEX and DairyChainEnergy, all predefined assessment criteria from our content-orchestration methodology were regarded as equally relevant for estimating the future success of DCFSs and DEMSs. Looking at the first assessment criterion (customer benefit), DCFSs were evaluated in May 2022 by decision makers from the German sector of farm machinery dealerships, who were in direct contact with end customers and responsible for their companies’ strategic portfolios. Because those interviewed experts have a cumulative market experience of 120.5 years and work at companies that already serve the NEVONEX platform as installation providers, both market expertise and familiarity with the product are given to formulate a valid assessment of customer benefits of DCFSs. In contrast to conducting direct customer surveys, as conducted for DEMSs in ref. [14], such expert surveys can give the advantage of providing insights on aggregated market trends and actual purchase behavior. To ensure compatibility with our methodology, regarding the results from both the end-customer surveys and expert interviews, the scale of given assessments was adjusted accordingly. Next, the societal impacts of DEMSs and DCFSs were reviewed with the help of literature insights, while the economic provider benefit was estimated as being equally moderate across all digital services. Lastly, input for the remaining assessment criteria (governance implications, technical feasibility, and resilience) were received in the context of interviews with industry experts, whereas DCFSs were mainly assessed by specialists from NEVONEX, and insights on DEMSs were received from more independent livestock-farming experts. After the evaluation of each criterion by the experts using five-point Likert scales, the results were aggregated for each DCFS and DEMS by forming the arithmetic mean for each service over all assessed criteria. As a result, a ranked list of digital services was derived for both NEVONEX and DairyChainEnergy (Figure 4).

4. Discussion

Our study results proved the applicability of our methodology for different agrisectors (crop farming and dairy) and types of agriecosystems (IoT agriecosystems and data agriecosystems) and created transparency for the prioritization of digital services for both cases (NEVONEX and DairyChainEnergy). The results indicate that “spot spraying offline” and “energy data marketplace” are the most promising digital services in the context of our methodology. Even though the execution of the methodology was time-intensive due to the necessity of setting up tailored questionnaires and selecting suitable experts, if planned correctly, the output from the interviews can not only be used in the context of our methodology but can also serve as valuable insights when implementing a digital service (e.g., when designing a technical infrastructure and making architectural choices [45]). In this context, the quantity and objectivity of experts assessing a digital service is critical [46]. In our study, for example, experts assessing the DCFS of NEVONEX were familiar with and involved in the development of the NEVONEX agriecosystem since its introduction in 2019 [47], and therefore could have been biased [46]. This could be one reason explaining why the DCFS of NEVONEX received, on average, a better rating compared to the DEMS in the scope of the DairyChainEnergy project. Our methodology, however, was not designed for, and hence is not applicable to, evaluating the probability of success among different digital farming solutions; it is only valid for assessing the digital services of one digital agriecosystem. This is reasonable because digital platforms differ in terms of value creation, the interdependency of platform participants, and growth patterns [31]. Hence, despite the higher rating of DCFSs compared to the DEMSs in the scope of our study, there is not a lower future success probability implied for DairyChainEnergy. One reason for this is that the target markets of both solutions differ in regard to forecasted market growth, technological conditions, and market reach of competitors. In general, the crop-farming industry is digitally more mature and interlinked than the livestock-farming sector. For example, there is an oligopoly in livestock farming, with six major players dominating the market and owning the interfaces on the physical layer. In comparison, for crop farming, the ISOBUS standard (ISO11783) has put pressure on dominant market players regarding the interoperability of their equipment. [48] Hence, a solution such as NEVONEX already benefits from an advanced interoperability, whereas the data basis for DEMSs in most German dairy stables is not yet collected automatically [8]. Against this background, the variety of established digital services in crop farming is much higher compared to the as-is portfolio of DEMSs, i.e., many farmers already have some experience with DCFSs, also in conjunction with other precision-farming technology [49]. In contrast, digital services in energy management for dairy farms might have appeared to be more abstract for our sample farmers and experts [14].
Looking at how the application of our methodology supported the future development of the two digital agriecosystems, it became apparent that the methodology is most helpful at an early concept stage. This is because digital agriservices show high variety (e.g., in terms of technical requirements and affected stakeholders), and hence have different demands on the set-up of the respective digital agriecosystem (e.g., on its IT architecture [45]). Thus, when there is clarity on the timeline of digital services to be implemented, which are already at an early concept stage, a misfit of platform capabilities and digital service requirements can be avoided, leading to higher cost efficiency and shorter implementation times, as well as to higher customer satisfaction. However, given that in digital ecosystems there can be different parties involved for the provision of platforms and digital services [1], our methodology can also be of high value for coordinating the ecosystem growth on a regular basis across business partners. To do so, interdependencies among digital services are to be analyzed and fundamental agreements on the implementation (including timeline and budget) must be made. In this context, stakeholders have to check whether the digital agriecosystem is able to fulfill the technical requirements of the prioritized digital services, whether synergy effects can be used when implementing cross-dependent digital services, and what set of digital services can be implemented under the given budget and capacity restrictions.
However, measuring the impact of our methodology on the future success of digital agriecosystems is challenging. First, this is because of a time shift between applying the methodology and collecting possible metrics (such as the number of service asset consumers or generated profit per digital service). Beyond that, given that a well-conducted content orchestration is only one factor for establishing a successful agriecosystem (Section 3.1), there is no possibility for quantifying the isolated impact of our methodology retrospectively. Nevertheless, learnings from the NEVONEX ramp down revealed how critical it is to manage content orchestration proactively, which is why our methodology should be leveraged as a best practice for managing the service portfolios of digital agriecosystems. In the case of NEVONEX, unfortunately, our methodology was applied too late; hence, even promising DCFSs from NEVONEX (Table 1) will no longer be available in the market.

5. Conclusions

The findings of this study will support providers of digital farming solutions in improving the content orchestration of their platforms and hence contribute to enhancing the digital service offerings in the sector. Insights from a real-life established and by now shut-down agriecosystem (NEVONEX) were leveraged to document first-hand knowledge on failure dimensions of digital agriecosystems. Although many of those failure dimensions can be avoided with the help of existing methods from the literature, no structured approach to orchestrate upcoming digital service portfolios was found, which raised the need to address this topic. The derived methodology for prioritizing digital services when setting up or expanding digital farming solutions applies a mix of techniques to evaluate digital services along six success criteria (customer benefit, society impact, economic provider benefit, governance implications, technical feasibility, and resilience). Based on the resulting ranking of potential digital ecosystem services, platform providers find guidance in prioritizing and facilitating the development and implementation of said services on their platforms. As a result, providers of digital agriecosystems now have a tested methodology at their disposal to optimize their digital service offerings and hence set their platforms up to successfully generate network effects.

Author Contributions

M.T. and T.T. contributed equally to this paper. Conceptualization, M.T. and T.T.; methodology, M.T., T.T. and S.G.; validation, M.T. and T.T.; formal analysis, M.T., T.T., S.G. and J.W.; investigation, M.T. and T.T.; resources, M.T., T.T. and H.B.; data curation, M.T., T.T., S.G. and J.W.; writing—original draft preparation, M.T. and T.T.; writing—review and editing, M.T., T.T. and H.B.; visualization, M.T., T.T. and S.G.; supervision, H.B.; project administration, M.T. and T.T.; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Koch, M.; Krohmer, D.; Naab, M.; Rost, D.; Trapp, M. A matter of definition: Criteria for digital ecosystems. Digit. Bus. 2022, 2, 100027. [Google Scholar] [CrossRef]
  2. Acs, Z.J.; Song, A.K.; Szerb, L.; Audretsch, D.B.; Komlósi, É. The evolution of the global digital platform economy: 1971–2021. Small Bus. Econ. 2021, 57, 1629–1659. [Google Scholar] [CrossRef]
  3. Better Food Ventures. Farm Tech Market Map: Why It’s Time to Distinguish Farm Tech from the Messy Supply Chain. 2020. Available online: https://betterfoodventures.com/agtech-landscapes/farm-tech-landscape-2020 (accessed on 20 April 2023).
  4. Dörr, J.; Nachtmann, M.; Linke, C.; Crawford, J.; Ehlers, K.; Balzer, F.; Gandorfer, M.; Gabriel, A.; Pfeiffer, J.; Spykman, O.; et al. Introduction. In Handbook Digital Farming; Dörr, J., Nachtmann, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2022; pp. 1–58. ISBN 978-3-662-64377-8. [Google Scholar]
  5. Azkan, C.; Möller, F.; Ebel, M.; Iqbal, T.; Otto, B.; Poeppelbuss, J. Hunting the Treasure: Modeling Data Ecosystem Value Co-Creation. In Proceedings of the Forty-Third International Conference on Information Systems, Copenhagen, Danmark, 9–14 December 2022. [Google Scholar]
  6. Quy, V.K.; van Hau, N.; van Anh, D.; Quy, N.M.; Ban, N.T.; Lanza, S.; Randazzo, G.; Muzirafuti, A. IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges. Appl. Sci. 2022, 12, 3396. [Google Scholar] [CrossRef]
  7. Treiber, M.; Oergele, J.-N.; Bell, D.; Bernhardt, H. The NEVONEX ecosystem—Impact assessment of a smart, neutral, open, and quality controlled framework for end-to-end infrastructure in agriculture. In Proceedings of the 2020 ASABE Annual International Virtual Meeting, St. Joseph, MI, USA, 13–15 July 2020; p. 1. [Google Scholar]
  8. Bernhardt, H.; Höhendinger, M.; Stumpenhausen, J. Development of the Technical Structure of the “Cow Energy” Concept. Agronomy 2021, 11, 1915. [Google Scholar] [CrossRef]
  9. Münzenmay, M.; Tanimou, M.; Kurre, H. Digital Ecosystem Nevonex for Smart Agriculture. ATZheavy Duty Worldw. 2020, 13, 44–49. [Google Scholar] [CrossRef]
  10. Mishra, S.; Liang, J.-M. Design and Analysis for Wireless Tire Pressure Sensing System. In New Trends in Computer Technologies and Applications; Hsieh, S.-Y., Hung, L.-J., Klasing, R., Lee, C.-W., Peng, S.-L., Eds.; Springer Nature: Singapore, 2022; pp. 599–610. ISBN 978-981-19-9581-1. [Google Scholar]
  11. Shumakova, O.; Pomogaev, V.; Skosyreva, N.; Vasyukova, M. Potential of Youth in Digital Ecosystem of Agricultural Sector. WEB 2021, 18, 1311–1325. [Google Scholar] [CrossRef]
  12. Migende, J.; Sonnen, J.; Schauff, S.; Schill, J.; Mayer-Bosse, A.; Leeb, T.; Stangl, J.; Stöcklin, V.; Kiefer, S.; Pessl, G.; et al. Agronomy Perspective. In Handbook Digital Farming; Dörr, J., Nachtmann, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2022; pp. 191–276. ISBN 978-3-662-64377-8. [Google Scholar]
  13. Oliveira, M.I.S.; Barros Lima, G.d.F.; Farias Lóscio, B. Investigations into Data Ecosystems: A systematic mapping study. Knowl. Inf. Syst. 2019, 61, 589–630. [Google Scholar] [CrossRef]
  14. Theunissen, T.; Keller, J.; Bernhardt, H. Mind the Market Opportunity: Digital Energy Management Services for German Dairy Farmers. Agriculture 2023, 13, 861. [Google Scholar] [CrossRef]
  15. Sobirov, B.; Yekimov, S.; Sitkovska, A.; Iushchenko, L.; Dmytrenko, R. Using Digital Ecosystems in Agriculture. In Ecosystems without Borders; Polyakov, R., Ed.; Springer International Publishing: Cham, Switzerland, 2022; pp. 171–176. ISBN 978-3-031-05777-9. [Google Scholar]
  16. Elijah, O.; Rahman, T.A.; Orikumhi, I.; Leow, C.Y.; Hindia, M.N. An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges. IEEE Internet Things J. 2018, 5, 3758–3773. [Google Scholar] [CrossRef]
  17. Arnold, L. Nevonex GmbH Stellt Ende März 2023 Geschäftstätigkeiten ein. 2023. Available online: https://www.agrartechnikonline.de/news/nevonex-stellt-taetigkeit-ein/ (accessed on 22 March 2023).
  18. Deter, A. Digitale Agrarhandelsplattform Agrando Meldet Vorläufige Insolvenz an. 2022. Available online: https://www.topagrar.com/markt/news/digitale-agrarhandelsplattform-agrando-meldet-vorlaeufige-insolvenz-an-13131228.html (accessed on 22 March 2023).
  19. Eckelkamp, M. Sun Sets on Granular Agronomy: New Digital Direction at Corteva. 2022. Available online: https://www.agweb.com/news/business/technology/sun-sets-granular-agronomy-new-digital-direction-corteva (accessed on 22 March 2023).
  20. Marston, J.; Burwood-Taylor, L. BREAKING: Bayer, Microsoft Formalize and Launch Cloud-Based Data Manager, Tools for Agriculture. 2023. Available online: https://agfundernews.com/bayer-microsoft-formalize-and-launch-cloud-based-data-manager (accessed on 22 March 2023).
  21. Rooney, J.J.; Heuvel, L.N.V. Root Cause Analysis for Beginners; Quality Progress: Milwaukee, WI, USA, 2014; pp. 45–53. [Google Scholar]
  22. Gawer, A. Digital platforms’ boundaries: The interplay of firm scope, platform sides, and digital interfaces. Long Range Plan. 2021, 54, 102045. [Google Scholar] [CrossRef]
  23. Steffen, B.; Möller, F.; Stachon, M. Success Factors of Digital Platform Design. ITM Web Conf. 2023, 51, 5001. [Google Scholar] [CrossRef]
  24. Cusumano, M.A.; Gawer, A.; Yoffie, D.B. The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power, 1st ed.; Harper Business: New York, NY, USA, 2019; ISBN 9780062896322. [Google Scholar]
  25. Gubanova, A.; Clasen, M.; Theuvsen, L. How do digital agricultural startups achieve critical mass? A qualitative analysis. In 37. GIL-Jahrestagung 2017-Fokus: Digitale Transformation–Wege in Eine Zukunftsfähige Landwirtschaft, Lecture Notes in Informatics; Gesellschaft für Informatik eV: Bonn, Germany, 2017; pp. 61–64. [Google Scholar]
  26. Theunissen, T.; Keller, J.; Bernhardt, H. Market gap for cross-value chain energy management in the German dairy sector. In Proceedings of the XX CIGR World Congress: Sustainable Agricultural Production—Water, Land, Energy and Food, Kyoto, Japan, 5–10 December 2022. [Google Scholar]
  27. Bürkel, J.; Zimmermann, A. Service-Dominant Design: A Digital Ideation Method. In Architecting the Digital Transformation; Zimmermann, A., Schmidt, R., Jain, L.C., Eds.; Springer International Publishing: Cham, Switzerland, 2021; pp. 143–162. ISBN 978-3-030-49639-5. [Google Scholar]
  28. Wortmann, F.; Ellermann, K.; Kühn, A.; Dumitrescu, R. Ideation for digital platforms based on a companies’ ecosystem. Procedia CIRP 2020, 91, 559–564. [Google Scholar] [CrossRef]
  29. Özcan, L.; Koldewey, C.; Duparc, E.; van der Valk, H.; Otto, B.; Dumitrescu, R. Why Do Digital Platforms Succeed or Fail?—A Literature Review on Success and Failure Factors. In Proceedings of the AMCIS 2022 Proceedings, Twenty-Eights Americas Conference on Information Systems, Minneapolis, MN, USA, 10–14 August 2022; Available online: https://aisel.aisnet.org/amcis2022/sig_dite/sig_dite/15 (accessed on 22 March 2022).
  30. Nass Bauer, C.; Trapp, M. Tangible Ecosystem Design. In Proceedings of the Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, CHI ‘19: CHI Conference on Human Factors in Computing Systems, Glasgow, UK, 4–9 May 2019; Brewster, S., Fitzpatrick, G., Cox, A., Kostakos, V., Eds.; ACM: New York, NY, USA, 2019; pp. 1–5. ISBN 9781450359719. [Google Scholar]
  31. Smedlund, A.; Faghankhani, H. Platform Orchestration for Efficiency, Development, and Innovation. In Proceedings of the 2015 48th Hawaii International Conference on System Sciences. 2015 48th Hawaii International Conference on System Sciences (HICSS), Kauai, HI, USA, 5–8 January 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 1380–1388. ISBN 978-1-4799-7367-5. [Google Scholar]
  32. Bundeskriminalamt. Cybercrime: Bundeslagebild 2021. Available online: https://www.bka.de/SharedDocs/Downloads/DE/Publikationen/JahresberichteUndLagebilder/Cybercrime/cybercrimeBundeslagebild2021.pdf?__blob=publicationFile&v=6 (accessed on 29 March 2023).
  33. Wolfert, S.; Ge, L.; Verdouw, C.; Bogaardt, M.-J. Big Data in Smart Farming—A review. Agric. Syst. 2017, 153, 69–80. [Google Scholar] [CrossRef]
  34. AgGateway. ADAPT (Inter-Operability). Available online: https://www.aggateway.org/GetConnected/ADAPT(inter-operability).aspx (accessed on 20 April 2023).
  35. Allen, I.E.; Seaman, C. Statistics Roundtable: Likert Scales and Data Analyses; Quality Progress: Milwaukee, WI, USA, 2007; Volume 40, pp. 64–65. [Google Scholar]
  36. Corteva Agriscience. Corteva, NEVONEX Collaborate to Explore Advanced Digitally Enabled Crop Protection Opportunities. 2022. Available online: https://www.globalagtechinitiative.com/digital-farming/corteva-nevonex-collaborate-to-explore-advanced-digitally-enabled-crop-protection-opportunities/ (accessed on 20 April 2023).
  37. MyEasyFarm. Die Nevonex & MyEasyFarm-Lösung. 2021. Available online: https://www.myeasyfarm.com/de/solution-nevonex/ (accessed on 20 April 2023).
  38. Bosch. NEVONEX-Marktstart: Jetzt im Deutschsprachigen Raum Verfügbar. 2022. Available online: https://www.bosch-presse.de/pressportal/de/de/nevonex-marktstart-jetzt-im-deutschsprachigen-raum-verfuegbar-241409.html (accessed on 20 April 2023).
  39. Farmers Weekly. How Future Digital Tools Could Transform Crop Decision-Making. 2020. Available online: https://www.fwi.co.uk/arable/crop-management/how-future-digital-tools-could-transform-crop-decision-making (accessed on 20 April 2023).
  40. Profi. Nevonex Startet Durch. 2022. Available online: https://www.profi.de/aktuell/aktuelle-meldungen/nevonex-startet-durch-29628.html (accessed on 20 April 2023).
  41. Google Play. NEVONEX Cockpit. Available online: https://play.google.com/store/apps/details?id=com.bosch.nevonexcockpit&hl=de&gl=US&pli=1 (accessed on 20 April 2023).
  42. GitHub. AgOpenGPS. Available online: https://github.com/farmerbriantee/AgOpenGPS (accessed on 20 April 2023).
  43. Reichhardt. Welcome to Reichhardt. 2023. Available online: https://www.reichhardt.com/us/ (accessed on 20 April 2023).
  44. Fendt. Die Telemetrie Lösung von Fendt: Fendt Connect. 2023. Available online: https://www.fendt.com/de/smart-farming/telemetrie#fendt-smart-connect (accessed on 20 April 2023).
  45. Sekaran, K.; Meqdad, M.N.; Kumar, P.; Rajan, S.; Kadry, S. Smart agriculture management system using internet of things. TELKOMNIKA 2020, 18, 1275. [Google Scholar] [CrossRef]
  46. Smith, J.; Noble, H. Bias in research. Evid. Based Nurs. 2014, 17, 100–101. [Google Scholar] [CrossRef] [PubMed]
  47. Robert Bosch GmbH. NEVONEX Powered by Bosch: The Ecosystem for Smart, Digital Agriculture. 2019. Available online: https://www.bosch-presse.de/pressportal/de/de/nevonex-powered-by-bosch-das-oekosystem-fuer-die-smarte-und-digitale-landwirtschaft-199552.html (accessed on 29 March 2023).
  48. Auernhammer, H. Precision farming—The environmental challenge. Comput. Electron. Agric. 2001, 30, 31–43. [Google Scholar] [CrossRef]
  49. Miller, N.J.; Griffin, T.W.; Ciampitti, I.A.; Sharda, A. Farm adoption of embodied knowledge and information intensive precision agriculture technology bundles. Precis. Agric. 2019, 20, 348–361. [Google Scholar] [CrossRef]
Agriculture 13 01003 g001 550
Figure 1. Digital agriecosystems—Types, scope, and examples [3,4,6,7,9,13,14].
Figure 1. Digital agriecosystems—Types, scope, and examples [3,4,6,7,9,13,14].
Agriculture 13 01003 g001
Agriculture 13 01003 g002 550
Figure 2. Leveraged materials and methods to achieve this work’s research target [21,22].
Figure 2. Leveraged materials and methods to achieve this work’s research target [21,22].
Agriculture 13 01003 g002
Agriculture 13 01003 g003 550
Figure 3. Newly introduced three-phase content-orchestration methodology for digital agriecosystems.
Figure 3. Newly introduced three-phase content-orchestration methodology for digital agriecosystems.
Agriculture 13 01003 g003
Agriculture 13 01003 g004 550
Figure 4. Results of the methodology test—Assessment of DCFSs and DEMSs within the scope of this study.
Figure 4. Results of the methodology test—Assessment of DCFSs and DEMSs within the scope of this study.
Agriculture 13 01003 g004
Table
Table 1. Description and implementation status of ten DCFSs from NEVONEX within the scope of this study.
Table 1. Description and implementation status of ten DCFSs from NEVONEX within the scope of this study.
Digital Crop-Farming
Service (DCFSs)		DescriptionImplementation Status (before NEVONEX Ramp Down)
Spot-spraying offlineDrone scans crop—spot-spraying application map is created in the cloud and transferred to conventional sprayer wirelessly via NEVONEX data infrastructure. NEVONEX task controller (TC) executes the application map and sprayer applies active ingredients. Site-specific, based on the identified weeds from drone survey.Proof of Concept (POC) [36]
Fleet managementLogging of machine data (e.g., position, fuel consumption, etc.) with upload to cloud and display in front end.Commercially available [37]
Tire pressure controlImproved automatic control of tire pressure from a unified user interface (UI).Commercially available [38]
Setup assistantSeeds, fertilizers, and active ingredients (that are equipped with a QR code) are scanned so that implements are adjusted according to the properties of the goods (e.g., scanning pesticide and sending information about buffer zones to sprayer [39]).Not implemented
ISOBUS terminalTC for the handling of application maps.
VT (virtual terminal) for operating ISOBUS implements available on tablet in NEVONEX cockpit app.		TC commercially available in “geoNex App” and “Xarvio Digital Service” [40]
Maintenance and service assistantCAN BUS and system diagnosis. Service history is made available to local machine dealers (manufacturer independent). Remote CAN-BUS diagnosis and pop-up notifications (e.g., for greasing intervals).Partly commercially available as function of NEVONEX Cockpit App [41]
Automated process data acquisitionAutomated logging, cleaning, and management of process data from agrimachinery, including external sensor information and machine implement identification.POC
Automatic guidanceVisually assisted manual guidance system, comparable to AgOpenGPS [42] or Reichardt Smart Guide [43].Not implemented
N-sensor liquid manureNIR sensor scans crop and adjusts application rate of liquid manure in a map-overlay approach for a variable rate application on the fly.Not implemented
NEVONEX onboardIn-cabin customizable dashboard (including widgets); in-field synchronization between cooperating machines (compare Fendt Smart Connect [44]).In development
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Treiber, M.; Theunissen, T.; Grebner, S.; Witting, J.; Bernhardt, H. How to Successfully Orchestrate Content for Digital Agriecosystems. Agriculture 2023, 13, 1003. https://doi.org/10.3390/agriculture13051003

AMA Style

Treiber M, Theunissen T, Grebner S, Witting J, Bernhardt H. How to Successfully Orchestrate Content for Digital Agriecosystems. Agriculture. 2023; 13(5):1003. https://doi.org/10.3390/agriculture13051003

Chicago/Turabian Style

Treiber, Maximilian, Theresa Theunissen, Simon Grebner, Jan Witting, and Heinz Bernhardt. 2023. "How to Successfully Orchestrate Content for Digital Agriecosystems" Agriculture 13, no. 5: 1003. https://doi.org/10.3390/agriculture13051003

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop