In the precision farming (PF) literature on innovation activities, it becomes apparent that only individual aspects of the entire PF innovation process chain are considered, namely, the knowledge transfer and the adoption of PF applications. Therefore, this study seeks to analyze the innovation mechanisms in the entire PF innovation process chain. The paper identifies potentials, barriers and challenges for PF innovations in Germany and the respective agricultural subsector plant production. An in-depth understanding of innovation mechanisms is required to enhance innovation capabilities, overcome obstacles and bring further innovations to the agricultural field. A mix of qualitative and quantitative methods was applied—including interviews, an expert workshop and a Delphi survey—to explore innovation mechanisms and the role of heterogeneous actors. The research is based on the analytical framework of the sectoral innovation system approach. Key results are the identification of barriers in the later stages of the innovation processes (including validation, serial production and adoption), a gap in the knowledge transfer between science and practice, insufficient communication and co-operation between actors and the important influence of political and legal conditions. Furthermore, this study showed that farmers play an important role in the generation of innovations. For example, farmers are not only adopters or demanders but also impulse providers or co-developers. In conclusion, this study moves the PF innovation debate forward not only by providing adoption facts but also by presenting explanations for the complex interactions between actors throughout the innovation process chain.
Currently, precision farming (PF) is one of the most innovative fields in plant production. Precision farming describes a technological regime (cf. Breschi et al. 2000) rather than a set of individual innovations or technologies. Thus, PF can be defined as holistic crop management concept (Kutter et al. 2011). Technical PF innovations promise to enhance the competitiveness of plant production. In addition, PF innovations are potentially capable of coping with global and future challenges, such as: food security by increasing food production; environmental problems by sustainable use of resources and consumer demands by securing product traceability and food quality (e.g. Pedersen and Kirketerp Scavenius 2011; Ancev et al. 2005; Yu et al. 2003). To meet these objectives, further innovations are required that can be successfully established in markets (European Commission 2012). Thus, PF is an important field of science and has been intensively researched in the last 15–20 years. In particular, European researchers have carried out large joint projects, such as pre agro (Oliver and Stafford 2009) and FutureFarm (Pedersen and Kirketerp Scavenius 2011). The content of these projects ranges widely from technologies to impact and acceptance.
Generally, three different PF research focus areas are represented in the literature. First, studies aim to prove the profitability and the positive environmental impacts of PF (e.g. Shockley et al. 2012; Wang et al. 2012; Havlin and Heiniger 2009; Kroulík et al. 2009; Ancev et al. 2005; Yu et al. 2003; Hatfield 2000). The second important research subjects include numerous investigations focused on the technical aspects of product development and process improvement (e.g. overviews by Stafford and Werner 2002; Zhang et al. 2002; Auernhammer 2001). Finally, the third important research subject centers on implementing PF at the single farm level. For example, the German study by Reichardt and Jürgens (2009) dealt with farmer experiences and barriers for adopting PF technologies. Reichardt et al. (2009) showed how PF knowledge can best lead to its adoption and transfer into practice. Kutter et al. (2011) argued that communication and co-operation strategies of the farmers during the PF adoption process can vary depending on farm attributes. The acceptance and adoption of PF applications by farmers in Northeastern Germany were analyzed by Sattler and Nagel (2010), along with other conservation measures. Beyond the German context, Lawson et al. (2011) analyzed the perception and adoption of precision farming practices in four European countries and the potential time-savings by using farm information management systems. Adrian et al. (2005), Fountas et al. (2005), Batte and Arnholt (2003), Pedersen et al. (2004, 2001) and Daberkow and McBride (2003) conducted studies regarding the adoption, acceptance and diffusion of PF in different countries. Reetz (2002) and Heiniger et al. (2002) focused on PF education strategies and knowledge base and discussed the role of conferences, workshops and field days in the United States.
Considering the theoretical innovation literature that is relevant to this study, the work of Rogers (2003) is vital for explaining the adoption processes, or so-called diffusion. The author distinguished between different diffusion stages, including innovators, early adopters, the late majority and laggards. A large number of PF adoption studies use Rogers’ diffusion theory. Kline and Rosenberg (1986) described innovation processes other than diffusion theory. These authors presented a chain-linked model, which reveals that innovation processes are not linear and include manifold feedback-loops in most cases. The evolution from linear models to a systemic view on innovation processes is illustrated as well in Chaminade and Edquist (2010). Knickel et al. (2009) and SCAR (2012) described this evolution of innovation perspectives for the agricultural sector. The recent conception of Agricultural Knowledge and Innovations Systems (AKIS) is oriented by the system innovation perspective. Due to its historical background, AKIS focuses on publicly financed research, advisory service system and education. Thus, it disregards important actors of the agro-food chain (SCAR 2012). Especially in the PF innovation field, additional actors, such as agricultural engineering firms and its input suppliers, participate and directly influence innovation processes. To overcome the shortcoming of AKIS and to apply at the same time the system innovation perspective, the analytical framework of the paper is based on the innovation system theory of Malerba (2002, 2004). The author provides a holistic and systemic view on the sectoral level and examines a broad range of actors. According to Malerba, a sectoral system is composed of heterogeneous agents (e.g. entrepreneurs, consumers and scientists) who create, produce and sell a set of new and established products for specific uses. These agents form and use a problem-specific knowledge base. Additionally, a sectoral system contains specific technologies, inputs and demands, which are generated and managed by these agents. “Agents are characterized by specific learning processes, competencies, beliefs, objectives, organizational structures and behaviours. They interact through processes of communication, exchange, co-operation, competition and command, and their interactions are shaped by institutions (rules and regulations)” (Malerba 2002). On the basis of Malerba’s approach, Koschatzky et al. (2009) identified six key elements of a sectoral innovation system, including (1) agents and organizations, (2) mechanisms of interactions and intermediates, (3) knowledge base and human capital, (4) institutions and public policy, (5) technologies and demand and (6) national and global competition. These innovation elements have guided the structure of this analysis. Malerba’s approach describes primarily the components and the structure of an innovation system. To gain process-oriented insights of innovation activities, as well on small scale level and to identify potentials and barriers, it is necessary to emphasize the interplay of the innovation system components and the output of innovation activities. Correspondingly, the last paragraph of the results section is devoted to the synthesis on the case-study level, called “innovation processes”. In this context, Hekkert et al. (2007) commented that innovation system conceptions that mainly focus on its structure are insufficient because of neglecting dynamics of innovation processes at the micro level.
In the PF literature on innovation activities, it becomes apparent that only individual aspects of the entire PF innovation process chain are considered, namely, the knowledge transfer and the adoption of PF applications. However, this study assumes that the PF innovation process chain is a feedback-linked process from impulses for PF innovations (e.g. by farmers or other sectors) through R&D of prototypes and the market launch to the diffusion of PF applications. Thus, this empirical study seeks to analyze the innovation mechanisms in the entire PF innovation process chain. By applying Malerba’s innovation system approach, the study considers the complex structure of actors and intermediates and their roles and interactions in all stages of the innovation processes. In addition, it considers the impacts of the regulations and funding programs. The objective of this research is to identify potentials, barriers and challenges for PF innovations and the respective agricultural subsector plant production. It is only possible to enhance innovation processes and overcome barriers by gaining an in-depth understanding of the innovation processes.
Materials and methods
Normally, the performance of a sectoral innovation system is measured by quantitative and output-orientated indicators such as patents, licenses and company research and development (R&D) (Smith 2005; Nagaoka et al. 2010). However, exhaustive and continuous statistical data do not exist for the German plant production or even for the entire agricultural sector. In addition, using Malerba’s approach and its adaption for the agricultural sector requires additional qualitative data of innovation activities beyond indicators and statistics (e.g. the role of actors and how they interact) to describe the (sub)sector innovation processes and mechanisms. Although Malerba did not suggest a methodical procedure for meeting the objectives of his concept, the research structure and design must be orientated toward these conditions. Thus, the authors decided to use an explorative approach. Commonly and in this case, explorative studies are carried out if data conditions are poor and if the field of investigation is complex and fairly unknown (Patton 2002).
Therefore, the following mix of qualitative and quantitative methods was used: (a) the small-scale level evidence for the PF case study was derived from expert interviews, secondary data analysis and a PF literature review; (b) an expert workshop was held to qualitatively validate the results for the subsector plant production; and (c) the Delphi, a two-step-survey, was used to obtain quantitative evidence at the subsectoral level.
Sixteen semi-structured interviews were held in 2011. Most of the interviews were conducted face-to-face, but some were conducted by phone (see Patton 2002; Liebold and Trinczek 2002). On average, the duration of each interview was approximately 1 h. Half of the interviews were sampled along the innovation process chain of a concrete PF sensor product. The other half was realized with different agents in their function as experts, including arable farmers, scientific research institutions, agricultural engineering industries (input suppliers), government advisory services and GIS/Software services. In this sense, experts are defined as individuals who possessed a knowledge advantage in a certain limited field and who had strategic thinking capabilities (e.g. abstract from a single problem) (Meuser and Nagel 2009, 2005). In communication situations, their function as experts comes to the forefront while their personal biography slides into the background (ibid.). To tap the exclusive knowledge of experts (Meuser and Nagel 2009, 2005), an interview guideline with open-ended questions was created. This questionnaire was divided into parts A and B. Part A involved questions related to the case study PF, and part B included a general assessment of the German agricultural innovation system. As recommended by Liebold and Trinczek (2002), each interview was recorded and transcribed. The qualitative data analysis software MAXQDA (VERBI GmbH, Berlin, Germany) was used to translate the interview data into codes for qualitative evaluation. The key topics that were considered included the innovation elements from Malerba (2002, 2004) and the factors that foster or inhibit PF innovations.
Because high-ranking experts were sampled and the interest in the “innovations in agriculture” topic is generally growing, the willingness to be interviewed was very high. Furthermore, most experts asked to be informed about the research findings.
For the 2011 workshop, 25 experts in plant production (farmers, input suppliers, scientists, advisors, association employees) were invited. Eight of the invited experts participated. Based on the expert knowledge, the objective of the workshop was to validate the interview results and their aggregation on a subsector level. Thus, the experts identified for each innovation element (according to Malerba 2002, 2004) sticking points of the expert interviews. These sticking points were selected considering their importance and their need for additional explanation. The workshop experts discussed how far interview results could be generalized for the subsector plant production.
The 2011 survey was conducted in two steps with 48 plant production experts. Concerning group size, diverging statements are found in the literature. However, expert panels below 50 participants are very common and are generally considered reliable (cf. Häder 2009). According to the deliberate selection method, the contacted experts represent all actors in the innovation process chain (Table 1). The great majority of responding experts had more than 10 years of work experience in their field. In the first wave, 27 of the 48 individuals who were contacted returned usable questionnaires (a response rate of approximately 56 %). In the second wave, 25 usable questionnaires were returned. This corresponds to a response rate of 92 % of the first wave participants. This high response rate suggests that experts were very interested in the topic and were successfully selected and addressed. (Rates of approximately 30 % for the first and 70–75 % for the second wave are usual, as noted by Häder 2009).
The objectives of the Delphi survey were to (1) verify and quantify the interview results and (2) to obtain an assessment of the plant production subsector innovation activities. According to Häder (2009) (also see Linstone and Turoff 1975; Pill 1971), the Delphi method characteristics include the following: a multi-step-survey with a standardized questionnaire, questioning of experts, anonymity of single questions, a statistical group response, controlled feedback and iteration of the survey. The size and composition of the expert pool depend on the survey aim, the available resources and on the panel mortality expectation (Ammon 2005). In contrast with other surveys, the selection of the panel participants is intentional and target-oriented rather than randomized.
A questionnaire that contained 14 questions in the 1st wave and 15 in the 2nd wave was generated. The questionnaire design of the Delphi is based on the analytical framework of Malerba (2002, 2004). Mainly closed questions were used that asked about the following: (1) demographic and professional background, (2) important innovation system actors and their function, (3) factors that inhibit and foster the innovation process and (4) an estimation regarding future technology trends and skilled labor-force demand.
Statistical data from the closed questions were cleaned and analyzed with the SPSS software for Windows (IBM SPSS, Chicago, IL, USA), employing mainly descriptive statistical methods. For the aggregation of answers and the creation of categories for the open questions, the qualitative content analysis method of Mayring (1997) was applied. In the 2nd wave (feedback round), the participants received a summary of the results from the 1st wave of the survey that contained diagrams and short texts.
Agents and organizations
This innovation element contains information about relevant firm and non-firm organizations and their roles in different innovation process stages.
The interviewees in the round of expert interviews (hereafter referred to as “interviewed experts”) identified agricultural engineering firms and agricultural enterprises (farms) as the most relevant firm groups in the innovation field of PF. However, agricultural service supply agencies (as distribution partners and input suppliers for agricultural engineering firms) play a minor role in innovation activities. The most important non-firm organizations are universities and research institutes, which focus mainly on long-term and fundamental research. Sometimes, these institutes also undertake contract research for companies. By contrast, the R&D activities in firms are normally geared toward the rapid development of practical and market-orientated products. Large companies with their own R&D department are often the only ones with long-term R&D projects. In product development projects, agricultural engineering firms intensively cooperate with the external sector supplier industry (e.g. specialists in optics, electronics, information and communication technologies). These industries contribute their specialized knowledge to advancing PF.
The expert interview findings were supported by the Delphi survey for the subsector level of plant production. According to the Delphi experts (hereinafter referred to as “surveyed experts”), agricultural enterprises/farmers (76 %), the input suppliers (64 %) and research (44 %) are the most important impulse providers for innovations. However, downstream branches such as the food industry and food retailing are rather or very unimportant. In addition, consumers and associations (farmer unions, etc.) are not relevant impulse providers for innovations (Fig. 1).
Regarding the role of agricultural enterprises in the innovation process, farmers are primarily innovation users, but they are also impulse and feedback providers for scientists and input suppliers when they express concrete problems or propose improvements for existing products to agricultural engineering firms. Furthermore, the co-operation between the producers and users offers opportunities by leading to mutual learning processes for the actors and, in the best case, to incremental innovations. Experts stated in the workshops that one great potential of subsector plant production is that a considerable number of farmers have open minds about innovations. This open-minded attitude results mainly from the on-going generational change in farm management to young and highly qualified farmers who are interested in technology. However, only a few farmers act as innovators or developers. Thus, one identified obstacle is that development ideas by farmers nearly always remain at the invention level. In other words, these ideas represent individual solutions for a single farm and rarely become a market-orientated product.
When asked about the function of farmers in agricultural innovation systems, the Delphi experts agreed that plant production farmers are mainly users of innovations (Table 2). In this case, the respondents are unanimous. However, the other functions of farmers in the innovation system were not clear (a high percentage of “undecided” answers). It is worth mentioning that 39 % of the experts indicated that farmers are very or rather important as tinkerers/inventors, but only 17 % indicated that they are rather or very important innovative entrepreneurs.
Interactions and intermediates
This innovation element refers to the structures of relationships among heterogeneous agents (hereinafter referred to as “actor”) and networks. In addition, this element illuminates the role of intermediates and their function in knowledge transfer.
Most interviewed experts commented that the number of agents in the PF field is relatively small. However, co-operation and multiple relationships among scientists, industrial firms and some agriculture practices and intermediate organizations are established through joint development, projects, networks or client relationships. Beyond common innovation activities, the exchange of information normally occurs from personal interactions between the heterogeneous actor groups at trade fairs, conferences and events such as seminars, workshops and training courses. Intermediate organizations, associations such as DLG (German Agricultural Society) or KTBL (Association for Technology and Structures in Agriculture) and independent public advisory services are all extremely important for farmers due to their decision support provision (e.g. the certification and examination of agricultural engineering devices by DLG or by similar organizations has a signaling effect for farmers).
According to the interviewed experts, the quality of information and knowledge transfer between agents is diverse. Knowledge transfer works relatively well within science (for example, through conferences or publications) and, to a certain extent, through science and agricultural practice intermediates. However, there is a gap between obtaining scientific results from joint projects and transferring them directly to farmers. While some innovative farmers are involved in R&D or application-oriented projects, the majority are not aware of recent research findings. In addition, this knowledge transfer issue was discussed in detail by workshop experts at a subsectoral level and was rated as ambivalent. One deficit occurs because the actors disagree on what innovations are. Another challenge is that there is a considerable amount of diverse research approaches for concrete problems. Thus, different types of research approaches and scientific results often cause confusion or uncertainty for farmers when they consider adequate solutions. By contrast, potentials arise from independent and neutral advisory services (including training and testing institutes of the federal states) as intermediates for testing and informing farmers about appropriate technologies and from existing bridges between science and practice.
Although agricultural advisory services are not very important impulse providers, they were identified as a general fostering factor for plant production innovations because they create a supportive environment for innovations. The majority of the Delphi experts agreed with the interview and workshop experts that the agricultural advisory service system is rather fostering or very fostering (Fig. 2).
Concerning the knowledge transfer, the Delphi survey revealed that the existing system of performance evaluation in science is ambivalent. However, although approximately one quarter of the respondents reported a rather fostering influence, the majority reported an indifferent (“undecided”) or even inhibiting influence (Fig. 2). Furthermore, insufficient communication and co-operation between actors (research, input suppliers, farmers, etc.) and insufficient knowledge and technology transfer are two of the five most important barriers for innovations (Table 3).
Knowledge base and human capital
The innovation element “knowledge base and human capital” is related to such topics as availability of skilled staff, higher education, research landscape and learning processes.
Due to new technologies and service jobs in agriculture, this sector has become more challenging and demanding. Simultaneously, interviewed experts indicated that a skilled labor shortage for agricultural engineering firms, agricultural practices and advisory services may occur. First, the PF innovation field and agricultural engineering is competing with other sectors, such as machinery construction or automotive engineering, for qualified engineers, mechatronics engineers and others. Second, the increasing number of agricultural students and the availability of practice-oriented and extra occupational courses at applied science universities are positively rated because they could reduce the looming shortage of skilled labor in agricultural practices. While the research landscape in Germany was ranked as good by the interviewed experts, the agri-technical teaching and research capacity has decreased in the past in favor of a stronger focus on basic research. This trend could weaken the performance of the innovation system and the international competitiveness of Germany. Third, the interviewed experts looked critically at the availability of specialized PF advisory personnel in agricultural engineering firms and advisory institutions. Accordingly, in the chambers of agriculture, the ranges of PF agricultural services do not meet current needs. In addition, there is a lack of target-group specific information (e.g. profitability calculation at the single farm level). These findings were confirmed in the workshop.
According to the Delphi results, the lack of a skilled labor force for plant production and related agricultural businesses will become problematic in the near future (within 5–10 years). The majority of the Delphi experts stated that they expect a quantitative workforce shortage, especially in primary agricultural production and agricultural advisory services (Table 4). In response to the question regarding the disposition of employees with sufficient practical orientation, experts identified some serious problems in the near future. Primarily, they expect a lack in research, in the supplying industry (“input suppliers”) and in primary agricultural production (“farmers”) (Table 5).
Institutions and public policy
Regulations, norms, standardization and funding programs, which concern the innovation element “institutions and public policy”, affect the PF innovation field positively and negatively. Documentation and reporting requirements (e.g. documentation about field-specific use of means of production such as plant health products and fertilizer) and environmental legislation are important drivers for adopting innovations. However, quality assurance instruments, such as the Single Farm Payment, the European Water Framework Directive, Agri-environmental Measures and the Good Agricultural Practice (GAP) require additional time, effort and expenses for farmers and firms because of their demanding and extensive nature. The interviewed experts commented that this trend encourages farmers to either use specialized software and GPS or to outsource their data management. Nevertheless, this complexity and the increasing amount of on-farm data present a major challenge concerning the practical use of innovations. The longstanding discussion about the ISOBUS standard (as a partial application of ISO 11783 2009) and the lack of a common standardized interface were addressed by nearly all interviewees. This shows that such standards and norms can limit and promote innovation and diffusion processes. In the Delphi survey, the impact of formal standards, norms or certificates became clearer. In fact, 36 % of the respondents considered standardization/certification as a rather important or very important impulse provider for innovations.
Documentation requirements and certifications are incorporated into current laws or acts, such as consumer or environmental acts. Concerning the Delphi results, the majority of surveyed experts stated that plant production policies and legislation are less important impulse providers. The impacts of most agrarian policies and instruments on innovations, by and large, were viewed positively. The surveyed experts identified the Renewable Energies Act as the most fostering law for innovation activities in German agriculture, followed by the German Animal Welfare and Plant Protection Act and the Consumer Protection and Environmental Protection Act (Fig. 3). The Genetic Engineering Act was seen as the most inhibiting law. However, the experts did not observe a supporting influence from this act. By contrast, the impact of the Common Agricultural Policy was not clear; 60 % of the experts were undecided.
With regard to funding programs, the interviewed experts noted that third-party funding for projects is important for providing innovation activities. In addition, third-party funding is frequently used by scientists and companies, as well outside of the PF innovation field. Some of these experts added another angle. At times, large companies are more interested in project funding for networking than for financing. In the Delphi survey, the influence of funding programs was seen as more positive than negative. Thirty-two percent of the experts found that these programs are a relevant factor for success. Nearly the same number of experts chose the option “neither agree nor disagree” (Table 6). However, insufficient funding and finances were rated as the most important barrier in the innovation process (Table 3).
Technologies and demand
This element of analysis refers not only to basic innovation system technologies but also to questions of demand and adoption of innovations.
Farmers, as the main customer of agricultural technologies, are crucial for the success of innovations, especially during the market launch and the adoption process. Experts confirmed in the interviews and in the workshop that innovations could only be established successfully in markets if farmers see a clear economic benefit and practicability (simple use). In the case of PF applications, these benefits are not always obvious at the single farm level. Only some basic PF applications, such as the track guiding system, the GPS-based yield mapping or soil sampling from various manufacturers and distribution partners and a few sensor products are now used frequently by farmers. Thus, these products are available on the commercial market in larger quantities. Even if firms produce PF technologies or offer corresponding services in Germany, the diffusion of PF technologies to German farms is low. According to the interview and workshop experts, the diffusion of PF technologies is limited because some systems and applications are not yet sophisticated enough or have high investment costs (time factor included). For farmers, this causes additional adaptations and modifications of tools/applications and in the organisation of the farm. Furthermore, many PF applications are only reasonable and profitable for farms that are larger than a certain size. Therefore, agricultural contractor services or machinery rings, which normally have large areas under cultivation, adopt this technology early. By contrast, small farmers lack capital to undertake these larger investments. Overall, workshop experts calculate that half of German farmers tend to react conservatively to technical innovations. Thus, these farmers wait and precisely evaluate the costs, risks and benefits of technical innovations. However, the interviews and the workshop discussion showed that an increasing number of farmers (including small farmers) are interested in PF technologies, mainly because of rising equipment and agricultural raw material costs (e.g. fertilizer, fuel, feed, etc.). In addition, some experts stated that the propensity for farmers to innovate has increased along with their increased education in the last 20–30 years.
One crucial question in the Delphi survey asked about the success factors in the innovation process. The economic benefit and practicability of an innovation and the concrete demand for it from the market or practitioners appears to impact the success or failure of new technologies in plant production. These factors were identified as the two most important success factors (Table 6). Conversely, their absence creates an important barrier in the innovation process (Table 3).
Aspects within this innovation element are linked to processes of competition, market conditions and selection by input suppliers (PF engineering firms included) and farmers.
According to the interviewees, the German PF technology producers are generally very innovative and competitive in the global market. German producers have created a leadership role in terms of PF interface components. The majority of the interviewed experts suggested that Germany ranks among the leading nations in this field (in addition to, e.g. Denmark, the Netherlands, the United States and China). Although R&D and production are often localized in Germany, a large number of German agricultural technologies for plant production are exported to other countries. According to the interviewees, simple agricultural machinery and equipment products, such as tractors, are exported from Germany more often than complex and complicate PF applications. Furthermore, the interviewed experts stated that global players appreciate locational advantages in Germany for carrying out R&D. These advantages include the availability of qualified staff, transportation and research infrastructure. However, due to specific business structures and different market environments, it is challenging to penetrate international markets. Nonetheless, the scarcity of agricultural land in Europe is an important driver for the R&D of highly intelligent solutions in Germany and other countries in Europe. By contrast, some interviewees argued that the main focus of R&D in the United States, Canada and South America is on large-scale agricultural land use technologies.
Concerning German market conditions for PF innovations developments, the experts in the interviews rated as inhibiting factor that the production of these technologies often is limited to small quantities. Furthermore, the Delphi survey asked about market-related factors that influence the innovation capability. At first glance, the specific economic structural conditions in plant production seem to inhibit innovations. 84 % of the surveyed experts stated that low profit margins in agricultural production and the low market demand from farmers as users of innovations negatively impact innovation activities. However, economic pressure may also positively influence innovation activities; 16 % of the experts considered low profit margins as a rather fostering factor (Fig. 2). In addition, international competition was one important observed impulse provider for innovation. The existence of small and medium-sized businesses and the spirit of entrepreneurship in Germany is a rather significant success factor in the innovation process and for the competitiveness of German agri-technical enterprises.
This paragraph illustrates the interplay of the individual innovation elements, to emphasize the process-oriented character of innovation activities.
Focusing on innovation activity dynamics, the PF case study, especially the development analysis of a concrete innovation, has shown that innovation processes are rarely linear and often time consuming. Nevertheless, the main stages in the innovation processes were identified as follows: basic research, applied research, prototype stage, testing and validation stage, serial production, market launch and the adoption and diffusion stage. The main impulses come rather from other sectors and less from fundamental research. Thus, initial or fundamental innovations, such as GPS or sensor technologies, were developed in other sectors (military, optic/sensoric, information and communication technologies) and were then applied and adopted in agriculture. Some impulses stem directly from agricultural practices that primarily concern incremental innovations. In addition, the current agricultural labor force shortage creates a need for new technologies and process innovations. However, as in other sectors, there are companies in the plant production subsector that artificially stimulate and create demand for new products.
Interviewed experts stated that it takes a very long time to develop a marketable serial product (up to 10 years in PF). Thus, a prototype is still far from being a finished product that can be used by farmers. Several feedback loops (for example, from PF users or testing and research farms) are generally necessary for a successful innovation. Sometimes, an innovation idea remains at the prototype stage for years or never becomes a marketable product. The interviewed experts and the workshop participants indicated that the high cost risks for companies to finance long-term validation and the uncertainty of product sales covering R&D costs causes this delay. Additionally, a meaningful validation requires a long period of time due to the specific plant production operating conditions (operating with living organisms, cycle of vegetation, weather conditions, etc.).
The German federal government and the EU funding programs primarily support the R&D of innovations during the early stages of the innovation process, from fundamental research to prototype. However, in later stages (validation, market launch and diffusion), interview and workshop experts identified that consistent financing was lacking and that firms were often inadequately informed about suitable funding sources.
This finding was further substantiated by the results from the Delphi survey. Thus, lack of capital resources, in terms of an inadequate financial situation, was rated as the most critical barrier in the innovation process. Figure 4 illustrates the innovation process barriers that were primarily observed in the last innovation stages. Greater problems occur in the market launch and adoption and diffusion stage. From the Delphi experts’ point of view, the innovation system works well only in basic research, for this stage they identified no (or only small) barriers.
Agents and organizations
Malerba (2002) indicated that firms are the key actors in sectoral innovation systems. This statement was confirmed by the study findings. In the context of PF, agricultural engineering firms and agricultural enterprises (farms) are the most relevant firm groups. However, research institutions are the main actor among non-firm organizations. Concerning the role of these three core actors, the study demonstrated that R&D in private sectors focuses mainly on commercialization and refinement of innovations. However, universities, as important elements in the research landscape, are dedicated more to discovery and basic research. Sunding and Zilberman (2001) provided similar explanation for the entire agricultural sector.
A crucial result of this study is that farmers have multiple functions in the innovation system and perform in various stages of the innovation processes. Their most important role as innovation users in the adoption process is obvious and discussed extensively in the literature (e.g. Kutter et al. 2011; Reichardt and Jürgens 2009; Reichardt et al. 2009; Sattler and Nagel 2010; Lawson et al. 2011; Adrian et al. 2005; Fountas et al. 2005; Batte and Arnholt 2003; Pedersen et al. 2004, 2001; Daberkow and McBride 2003). However, this body of research does not normally recognize farmers as important actors in the entire innovation process chain. The active involvement of farmers is widely underrated although their numbers are low. The study showed that at the initial innovation stages, crop farmers serve as impulse providers for innovations by expressing their needs for solutions and their ideas for improving existing products. Nevertheless, Sunding and Zilberman (2001) stated that the function of farmers as an impulse provider has been reduced with time and that currently, the impulses given by research institutes and by the R&D of firms are more important. Concerning the next innovation stages, the study argued that farmers ideally test prototypes in field studies and provide feedback for scientists and input suppliers. In conclusion, the proactive participation of farmers in the early innovation stages (e.g. in the development of prototypes) creates opportunities for successful innovations in which market-orientated and user-friendly products can be developed. The main potential lies with the innovative and open farmers.
Interactions and intermediates
Reichardt and Jürgens (2009) emphasized that advisory services are important for the awareness and adoption of PF technologies. In this study, it was shown that public advisory services are not relevant impulse provider of innovations, but are important intermediates. They facilitate decision marking by farmers and provide neutral information about technologies or proceedings. Thus, these services can contribute to the knowledge transfer between science and agricultural practice. Due to their capacities, the official advisory service entities (including the testing and learning institutions of the federal states) offer new potentials to foster innovation activities, even in earlier stages of innovation. For example, these services can help to transmit suggestions by farmers for improvements in innovation to R&D or to test prototypes. However, to reap its full benefits, the advisory service must offer more customized information and provide PF related training opportunities for farmers. Reichardt and Jürgens (2009) and Pedersen et al. (2004) share this viewpoint.
Concerning farmer interactions, the study stated that personal contacts are very important. Similarly, Kutter et al. (2011) identified colleagues, neighboring farmers and specialized journals as important sources for German farmers to inform themselves about PF innovation experiences. Furthermore, this study illustrates that KTBL is a relevant intermediate for people who are interested in PF and for bringing PF actors together. According to Reichardt et al. (2009), the KTBL (2010) information material is of high quality but only reaches trendsetting farmers. However, the authors of this study agree with the position by Reichardt et al. (2009) and Gunnesch-Luca et al. (2010) to target trendsetters in order to promote innovation diffusion, mainly because these trendsetters pass on information to other less innovative farmers. A similar argument is provided by Rogers (2003), who explained the role of “opinion leaders” in influencing others’ decisions. They have normally greater mass media exposure, closer contact with change agents, higher social status and more innovativeness (Rogers 2003). Opinion leaders with a positive attitude toward an innovation often belong to the first groups of adopters, “innovators” (comparable to Gunnesch-Luca et al. 2010 “trendsetters”), or to their followers, “early adopters”. Targeting trendsetters or early adopters becomes crucial because in general, farmers are reluctant to encourage other farmers to adopt precision farming applications (Lawson et al. 2011). One potential option that would pave the way for agricultural producers who are resistant to adopt an innovation is the demonstration of agricultural practices and technologies to change previous practices (Adrian et al. 2005; Heiniger et al. 2002; Rogers 2003).
Knowledge base and human capital
This study addressed the lack of qualified labor force in various actor groups, such as the advisory service, agricultural practice and science. Thus, a shortage of skilled PF advisory personnel was identified. Reichardt and Jürgens (2009) noted that the official advisory service, which is organized by the chamber of agriculture, provides widespread rather than specialized services. Their survey indicates that 60 % of the interviewed advisors did not offer any PF service. Approximately 42 % of the advisors presumed that most farmers are less interested in PF technology and did not need PF information services. In addition, these authors stated that many of the advisors are not convinced of PF; this could be addressed by PF training courses for the advisors.
In the long term, not only the current generation of advisors and farmers but also the high-quality education of the young should be targeted as the next generation of skilled employees. Currently, there is a deficit in suitable PF teaching materials, especially in vocational and technical schools and in institutions for further education (Reichardt et al. 2009).
To develop more demand-orientated, market-conform and user-friendly products, scientists should contribute their valuable practical experience. Unfortunately, the Delphi experts predicted that a lack of well-experienced science labor will occur in the next few years. One reason for this shortage is the concentration of science on basic research, which is a result of the actual performance evaluation of scientists and the emphasis of scientific institutions on refereed publications (cf. Wissenschaftsrat 2006; Isermeyer 2003).
At the same time, a labor shortage can have a positive effect on innovation activities, which also affects the development of PF technologies. Hayami and Ruttan (1985) offered a good example of this effect in their theory of induced innovation. They argued that a labor shortage in agricultural may induce labor-saving technologies.
Institutions and public policy
The presentation of the study results was focused on the long-standing and on-going absence of a common standardized interface. The insufficient compatibility between systems is a crucial hurdle for adopting PF innovations (claimed by various authors, including Reichardt et al. 2009; Reichardt and Jürgens 2009; Kitchen et al. 2002; Pedersen et al. 2004). For example, Reichardt et al. (2009) considered the compatibility-improvement, in addition to educational materials, as a key factor for a successful implementation of PF applications. Generally, these publications focus on the diffusion stage regarding technical standards. However, this study emphasizes that these standards can be also problematic in the entire innovation process and for other market actors.
An important finding of this study was that public and governmental demands (in the form of environment protection laws or product and process traceability tasks) foster innovations in the PF field. Similar conclusions were drawn by Ancev et al. (2005), Reichardt and Jürgens (2009) and Sørensen et al. (2010). For example, Ancev et al. (2005) explained that agriculture is currently under pressure to cope with social demands for enhanced environmental performance, traceability and accountability for product quality and safety. Precision farming can be seen as one method to address these demands. Furthermore, Sunding and Zilberman (2001) noted that environmental regulations force the use of more environment-friendly techniques. Sørensen et al. (2010) deemed necessary to conceptually develop improved and integrated Farm Management, to cope with external documentation requirements and the increasing amount of data.
In fact, literature does not exist regarding the PF innovation processes. In addition, there is a lack of “white” publications regarding PF project funding in Germany and in other countries. Concerning financing of innovations and the co-operation between public and private R&D in the United States, Sunding and Zilberman (2001) published a review about the creation and adoption of agricultural innovations. The authors noted that innovations are affected by public policies, such as research funding and extension activities. This statement is similar to the study results, which indicated that financing innovation projects by external institutions is crucial for scientific institutes, small or medium-sized companies and (in part) large companies.
Technologies and demand
Interviewed experts identified the most common PF applications among farmers, which included the track guiding system, GPS-based yield mapping or soil sampling and (to an extent) site-specific N fertilization. It was estimated that the simple and efficient applications that allow direct benefits were generally widespread at first, which correspond with related statements found in the PF adoption literature (Reichardt et al. 2009; Robertson et al. 2012). Precision Farming is not an easy-to-use technology (Batte and Arnholt 2003), but requires a continuous process of learning, information, decisions and management to profit from PF technologies (Robertson et al. 2012). Thus, the diffusion process underlies the complex and long-drawn-out pattern (cf. Rogers 2003). These reasons for dissemination correspond with questions regarding the influence factors in the innovation processes. The main success factors that were mentioned by the Delphi experts in association with the adoption stage were the economic benefit and the practicability of an innovation and an existing market demand. Accordingly, Daberkow and McBride (2003) and Adrian et al. (2005) concluded that the perception of PF as a profitable technology by farmers is a crucial factor.
The interview and workshop findings confirmed the main problems that were mentioned in publications that occur when adopting PF. Sattler and Nagel (2010), Reichardt et al. (2009) and Reichardt and Jürgens (2009) stated that time requirements, the compatibility problems and the high investment costs are the most inhibiting factors for German adoption. Similar points are made in the international literature. For example, Batte and Arnholt (2003) assumed that before having a widespread adoption of PF, research has to simplify the technology and to develop lower-cost and more reliable sources of data to support PF decisions.
Furthermore, the main factors that affect adoption are farm size, computer literacy, full-time farming, farm type and location (Daberkow and McBride 2003). Regarding the farm size, Reichardt and Jürgens (2009) stated that due to the high costs of the PF technology, at least 100 ha must be given before farmers consider the introduction of PF applications. Farm size and computer literacy as limiting factors were also stressed by the experts in the workshop and interviews. For example, experts stated that one opportunity to overcome the problem that only large farms can implement PF applications is to use contractor’s services. Reichardt and Jürgens (2009) revealed that almost 50 % of the interviewed PF users in Germany already use such services.
In summary, several publications exist regarding the adoption of innovations. However, in this study, it can be assumed that the issues that are mentioned in these publications are also important for the entire process chain of innovations. Thus, a product can only be characterized as an innovation after its market launch, and a successful innovation must survive on the market (cf. Maclaurin 1953; Trott 2002).
The aspects of competition are perceived by interviewed experts and by Delphi experts as a relevant determining factor and framework condition for the innovation system. Overall, the business and company perspectives are dominant. Nevertheless, the answers given by interviewed experts are often vague, which can be attributed to the assumption that only parts of the entire structure and functioning were captured. The structure and competition processes are very complex and cover various dimensions (e.g. the sectoral, regional, national or international dimension). In this context, global competition leads to increasingly blurry national boundaries. Thus, global players (and some medium-sized enterprises among them) compete with each other regardless of their parent company or head quarter locations.
Unfortunately, hardly any “white” literature exists regarding the competition processes and market conditions in the PF innovation field. Therefore, a more general literature was considered: The University of Goettingen in Germany and Ernst & Young carried out a joint study regarding agribusiness in Germany. Their study focused on company strategies for internationalization (see Theuvsen et al. 2010). It revealed that the specific economic structural conditions in plant production, such as low profits in agricultural practices and market demand, are inhibiting factors for innovations. However, Theuvsen et al. (2010) indicated that, from a company perspective, these factors only negatively affect agricultural engineering companies. Due to the strong dependency of these enterprises on the financial conditions of their customers (the farmers), they are vulnerable to agribusiness economic trends. Consequently, this branch hardly suffered during the financial crisis of 2008. In addition to increasing export activities, foreign subsidiaries and joint ventures are common strategic instruments of internationalization efforts that are taken by the supplying industry, such as agrochemical and agricultural engineering companies. These joint ventures are motivated to recover new markets, occupy strategic positions and make quality and innovation leadership efforts (Theuvsen et al. 2010).
The six system elements are a key strength of Malerba’s (2002, 2004) innovation system approach and are crucial for describing innovation systems. However, this study assumes that it is also important to analyze the interplay of these elements, which involves the characterization of the entire innovation process chain along with its influencing factors that emphasize its processual nature. The “innovation processes” paragraph of the result section described chronologically the genesis of innovations. The process chain of a concrete PF product at the micro-level was analyzed. This analysis demonstrated that the process was non-linear, occasionally included breakages or parallel developments and nearly always included feedback-loops. These findings were confirmed by the chain-linked model of Kline and Rosenberg (1986), which is more realistic than linear models (cf. Tohidi and Jabbari 2012). Kline and Rosenberg (1986) provided one of the most important responses regarding the use of linear models in the innovation processes, which was a common practice after World War II. Essentially, their model considers existing feedback and feed forward loops between the different stages, the decoupling of the science from the other stages and the contribution of a knowledge base. Processes are based on demand and needs rather than on basic research findings, which is assumed in linear science or technology push models (cf. Tohidi and Jabbari 2012). Thus, much more impulse providers exist than science (cf. Gulbrandsen 2009). Hekkert et al. (2007) expected non-linear innovation processes (labeled as “functions of innovation systems”) with multiple interactions between the functions. Indeed, these functions reflect the dynamic character of innovations but neglect the chronological sequence of interactions. The authors did not distinguish between different innovation stages. By contrast, in this study, the timing or the chronological sequence is considered as important to identify potentials and barriers in innovation processes.
The PF case study showed that impulses were derived from other sectors, were directly derived from agricultural practice or emerged from a special economic need. This last argument was also discussed by Sunding and Zilberman (2001) and was mentioned above in the innovation element “knowledge base and human capital” discussion. When describing agricultural innovation, Sunding and Zilberman (2001) preferred the already obsolete linear innovation model (with a market pull as starting point). Their model consists of the following stages: discovery, registration, development, production and marketing. It is not focused on visible actors, such as firms or non-firm organizations. Feedback loops and parallel developments are not included.
Apart from the innovation processes at a micro level, this study also revealed information at the meso-level, mainly regarding barriers in the different stages and typical plant production features. More problems occur in the later stages than in the earlier stages. Long-time validation, production in series and adoption are very costly and can cause uncertainty for suppliers. Plant production may have more barriers than other sectors: For example, plant production operates with special conditions (living organisms, cycle of vegetation, weather conditions, etc.), which hamper e.g. the validation. Furthermore, several products such as some PF applications are not marketable in a series (customized solutions are required) and the decision of farmers to adopt these products remains uncertain. Unfortunately, literature on the generation of innovations in agriculture, such as Sunding and Zilberman (2001) or Pardey et al. (2010), does not raise this issue. However, by discussing plant production specific barriers on innovation processes, this study addresses an important but new issue.
Generally, the main focus of this study was on technical innovations. Thus, the findings apply mostly to agricultural machinery of plant production. Different conditions most likely exist in other areas of the supplying industry, such as seed production or agricultural chemistry. In these areas, global players with market dominance, different lengths of innovation cycles and a higher impact, such as the national approval of varieties or plant protection products, might have a stronger influence. However, further research is needed to gain detailed insights about innovation processes in the other areas of plant production. The limitation of this study on technical PF innovations was necessary to handle the complex issue. At the same time the study results confirm the conclusions by Knickel et al. (2009) that technical innovations in agriculture normally go hand in hand with social and organizational changes and innovations. An examination of social and organizational innovations in the PF innovation field would provide a broader understanding of innovation mechanisms.
The study identified the potentials, barriers and challenges for innovation mechanisms in the PF innovation field and (to some degree) in plant production. These new insights along with a better understanding of innovation activities and mechanisms can contribute to overcoming barriers, enhancing a sectors’ performance and launching innovations onto the market. Furthermore, this study moves the PF innovation debate forward not only by giving facts about adoption (behavior and potentials) but also by presenting explanations about the complex interactions of important actors in the innovation process chain. It was shown that farmers play important roles of innovation adopters and impulse providers, but also of prototype testers or co-developer/inventors. Regarding knowledge transfer, the public advisory services (including the research and testing institutes) are an influential but underperforming intermediate with future potential. These advisory services and farmers can also contribute to the earlier stages of innovation. Barriers mainly appear in the later stages of the innovation processes. Some important causes of these barriers include a lack of financing for long-time validation and/or production, a low demand (mainly due to the lack of investment by farmers), insufficient communication/co-operation between actors and a knowledge transfer gap between science and practice. Generally, political framework conditions (acts, standards, certifications and funding programs) and the availability of a qualified labor force are important influencing factors for the PF innovation field’s innovation capacity and subsector plant production in Germany.
Adrian, A. M., Norwood, S., & Mask, P. (2005). Producers’ perceptions and attitudes toward precision agriculture technologies. Computers and Electronics in Agriculture, 48(3), 256–271.
Ammon, U. (2005). Delphi-Befragung. [Delphi survey]. In S. Kuehl & P. Strodtholz (Eds.), Methoden der Organisationsforschung. Ein Handbuch [Methods of organisational research. A handbook] (pp. 115–117). Reinbek: Rohwolt.
Ancev, T., Whelan, B., & McBratney, A. (2005). Evaluating the benefits from precision agriculture: The economics of meeting traceability requirements and environmental targets. In J. V. Stafford (Ed.), Proceedings of the 5th ECPA (pp. 985–992). Uppsala: Wageningen Academic Publishers.
Auernhammer, H. (2001). Precision farming—The environmental challenge. Computers and Electronics in Agriculture, 30(1–3), 31–43.
Batte, M., & Arnholt, M. (2003). Precision farming adoption and use in Ohio: Case studies of six leading-edge adopters. Computers and Electronics in Agriculture, 38(2), 125–139.
Breschi, S., Malerba, F., & Orsenigo, L. (2000). Technological regimes and Schumpeterian patterns of innovation. The Economic Journal, 110(463), 388–410.
Chaminade, C., & Edquist, C. (2010). Rationales for public policy intervention in the innovation process: Systems of innovation approach. In. R. E. Smits, S. Kuhlmann & P. Shapira (Eds.). The theory and practice of innovation policy. An international research handbook. Cheltenham: Edward Elgar.
Daberkow, S., & McBride, W. (2003). Farm and operator characteristics affecting the awareness and adoption of precision agriculture technologies in the US. Precision Agriculture, 4(2), 163–177.
European Commission. (2012). Communication from the Commission to the European Parliament and the Council on the European Innovation Partnership ‘Agricultural productivity and sustainability’. http://ec.europa.eu/agriculture/eip/pdf/com2012-79_en.pdf. Accessed June 1, 2012.
Fountas, S., Blackmore, S., Ess, D., Hawkins, S., Blumhoff, G., Lowenberg-Deboer, J., et al. (2005). Farmer experience with precision agriculture in Denmark and the US Eastern Corn Belt. Precision Agriculture, 6(2), 121–141.
Gulbrandsen, M. (2009). The role of basic research in innovation. In W. Østreng (Ed.), Confluence. Interdisciplinary communications 2007/2008 (pp. 55–58). Oslo: Norwegian Academy of Science and Letters, Centre for Advanced Study.
Gunnesch-Luca, G., Moser, K., & Kloeble, U. (2010). Adoption und Weiterempfehlung neuer Technologien: Die Bedeutung von Trendsetting. [Adoption and recommendation of new technologies: The meaning of trendsetting]. Der Markt, 49(1), 53-64. doi:10.1007/s12642-010-0026-7.
Häder, M. (2009). Delphi-Befragungen. Ein Arbeitsbuch [Delphi survey. A workbook] (2nd ed.). Wiesbaden: Verlag für Sozialwissenschaften.
Hatfield, J. (2000). Precision agriculture and environmental quality: Challenges for research and education. Resource document. The National Arbor Day Foundation. http://www.arborday.org/programs/papers/PrecisionAg.html. Accessed June 1, 2012.
Havlin, J., & Heiniger, J. (2009). A variable-rate decision support tool. Precision Agriculture, 10(4), 356–369.
Hayami, Y., & Ruttan, V. W. (1985). Agricultural development: An international perspective (2nd ed.). Baltimore: Johns Hopkins University Press.
Heiniger, R., Havlin, J., Crouse, D., Kvien, C., & Knowles, T. (2002). Seeing is believing: The role of field days and tours in precision agriculture education. Precision Agriculture, 3(4), 309–318.
Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74(4), 413–432.
Isermeyer, F. (2003). Für eine leistungsfähige Agrarforschung in Deutschland. Manuskript. [For a powerful agricultural research in Germany. Manuscript]. Braunschweig: FAL-Bundesforschungsanstalt für Landwirtschaft.
ISO—International Organisation for Standardization. (2009). ISO 11783 Tractors and machinery for agriculture and forestry—Serial control and communications data network, parts 1–14, Geneva.
Kitchen, N., Snyder, C., Franzen, D., & Wiebold, W. (2002). Educational needs of precision agriculture. Precision Agriculture, 3(4), 341–351.
Kline, S. J., & Rosenberg, N. (1986). An overview of innovation. In R. Landau & N. Rosenberg (Eds.), The positive sum strategy. Harnessing technology for economic growth (pp. 275–305). Washington D.C.: National Academy Print.
Knickel, K., Brunori, G., Rand, S., & Proost, J. (2009). Towards a better conceptual framework for innovation processes in agriculture and rural development: From linear models to systemic approaches. Journal of Agricultural Education and Extension, 15(2), 131–146.
Koschatzky, K., Baier, E., Kroll, H., & Stahlecker, T. (2009). The spatial multidimensionality of sectoral innovation: The case of information and communication technologies. Working Papers Firms and Region, R4. Karlsruhe: Fraunhofer ISI.
Kroulík, M., Kvíz, Z., Kumhála, F., Hůla, J., & Loch, T. (2009). Procedures of soil farming allowing to reduce compaction. Precision Agriculture, 12(3), 317–333.
KTBL. (2010). Automatisierung und Roboter in der Landwirtschaft. [Automatisation and roboter in agriculture]. KTBL-Tagung vom 21.-22. April 2010 in Erfurt, Germany: KTBL—Kuratorium für Technik und Bauwesen in der Landwirtschaft.
Kutter, T., Tiemann, S., Siebert, R., & Fountas, S. (2011). The role of communication and co-operation in the adoption of precision farming. Precision Agriculture, 12(1), 2–17.
Lawson, L., Pedersen, S., Sørensen, C., Pesonen, L., Fountas, S., Werner, A., et al. (2011). A four nation survey of farm information management and advanced farming systems: A descriptive analysis of survey responses. Computers and Electronics in Agriculture, 77(1), 7–20.
Liebold, R., & Trinczek, R. (2002). Experteninterview. [Interviews with experts]. In S. Kühl & P. Strodtholz (Eds.), Methoden der Organisationsforschung. Ein Handbuch [Methods and organisational research. A handbook] (pp. 33–71). Reinbek: Rohwolt.
Linstone, H., & Turoff, M. (1975). The Delphi method: Techniques and applications. Reading, MA: Addison-Wesley.
Maclaurin, W. R. (1953). The sequence from invention to innovation and its relation to economic growth. Quarterly Journal of Economics, 67(1), 97–111.
Malerba, F. (2002). Sectoral systems of innovation and production. Research Policy, 31(2), 247–264.
Malerba, F. (2004). Sectoral systems of innovation. Concepts, issues and analysis of six major sectors in Europe. Cambridge: Cambridge University Press.
Mayring, P. (1997). Qualitative Inhaltsanalyse—Grundlagen und Techniken. [Qualitative content analysis—Basics and techiques]. Weinheim: Beltz.
Meuser, M., & Nagel, U. (2005). ExpertInneninterviews—vielfach erprobt, wenig bedacht. Ein Beitrag zur qualitativen Methodendiskussion. [Interviews with experts - Often used, seldom discussed. A contribution to debates on qualitative methods]. In A. Bogner, B. Littig & W. Menz (Eds.), Das Experteninterview. Theorie, Methode, Anwendung [The expert interview. Theory, method, application] (2nd Ed., pp. 71–93). Wiesbaden: Verlag für Sozialwissenschaften.
Meuser, M., & Nagel, U. (2009). Das Experteninterview—konzeptionelle Grundlagen und methodische Anlage. [The expert interview—conceptual basics and methodological design]. In S. Pickel, G. Pickel, H.-J. Lauth & D. Jahn (Eds.), Methoden der vergleichenden Politik- und Sozialwissenschaft - Neue Entwicklungen und Anwendungen [Methods in comparative political and social science – New developments and applications] (pp. 465–479). Wiesbaden: Verlag für Sozialwissenschaften. doi:10.1007/978-3-531-91826-6_23.
Nagaoka, S., Motohashi, K., & Goto, A. (2010): Patent statistics as an innovation indicator. In B. H. Hall & N. Rosenberg (Eds.), Handbook of the economics of innovations (Vol. 2, pp. 1083–1127). Amsterdam (i.a.): Elsevier North-Holland.
Oliver, M., & Stafford, J. (2009). Editorial for special issue of papers on the German Preagro project. Precision Agriculture, 10(6), 488–489.
Pardey, P. G., Alston, J. M., & Ruttan, V. W. (2010). The economics of innoavtion and technical change in agriculture. In B. H. Hall & N. Rosenberg (Eds.), Handbook of the economics of innovations (Vol. 2, pp. 939–984). Amsterdam (i.a.): Elsevier North-Holland.
Patton, M. Q. (2002). Qualitative research and evaluation methods. Thousand Oaks: Sage Publications.
Pedersen, S., Ferguson, R., & Lark, M. (2001). A multinational survey of precision farming early adopters. Farm Management, 11(3), 147–162.
Pedersen, S., Fountas, S., Blackmore, S., Gylling, M., & Pedersen, J. (2004). Adoption and perspective of precision farming in Denmark. Acta Agriculturae Scandinavica Section B. Soil and Plant Science, 54(1), 2–6.
Pedersen, S., & Kirketerp Scavenius, I. (2011). Environmental impact with environmental indicators—with precision farming and controlled traffic systems. Resource document. FutureFarm report. http://www.futurefarm.eu/system/files/FFD5.6_Environmental_impact_final.pdf. Accessed June 1, 2012.
Pill, J. (1971). The Delphi method: Substance, context, a critique and an annotated bibliography. Socio-Economic Planning, 5(1), 57–71.
Reetz, H. F. (2002). Using conferences and workshops for technology training. Precision Agriculture, 3(4), 319–325.
Reichardt, M., & Jürgens, C. (2009). Adoption and future perspective of precision farming in Germany: Results of several surveys among different agricultural target groups. Precision Agriculture, 10(1), 73–94.
Reichardt, M., Jürgens, C., Kloeble, U., Hueter, J., & Moser, K. (2009). Dissemination of precision farming in Germany: Acceptance, adoption, obstacles, knowledge transfer and training activities. Precision Agriculture, 10(6), 525–545.
Robertson, M. J., Llewellyn, R. S., Mandel, R., Lawes, R., Bramley, R. G. V., Swift, L., et al. (2012). Adoption of variable rate fertiliser application in the Australian grains industry: Status, issues and prospects. Precision Agriculture, 13(2), 181–199.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.
Sattler, C., & Nagel, U. (2010). Factors affecting farmers’ acceptance of conservation measures—A case study from north-eastern Germany. Land Use Policy, 27(1), 70–77.
SCAR—Standing Committee on Agricultural Research. (2012). Agricultural knowledge and innovation systems in transition—a reflection paper. http://ec.europa.eu/research/bioeconomy/pdf/ki3211999enc_002.pdf. Accessed March 25, 2013.
Shockley, J., Dillon, C. R., Stombaugh, T., & Shearer, S. (2012). Whole farm analysis of automatic section control for agricultural machinery. Precision Agriculture, 13(4), 411–420.
Smith, K. (2005). Measuring innovation. In J. Fagerberg, D. C. Mowery, & R. R. Nelson (Eds.), Handbook of innovation (pp. 148–177). Oxford: Oxford University Press.
Sørensen, C. G., Fountas, S., Nash, E., Pesonen, L., Bochtis, D., Pedersen, S. M., et al. (2010). Conceptual model of a future farm management information system. Computers and Electronics in Agriculture, 72(1), 37–47.
Stafford, J., & Werner, A. (Eds.). (2002). Precision agriculture. Wageningen: Academic Publishers.
Sunding, D., & Zilberman, D. (2001). The agricultural innovation process: Research and technology adoption in a changing agricultural sector. In B. L. Gardner & G. C. Rausser (Eds.), Handbook of agricultural economics (Vol 1A Agricultural Production, pp. 207–261). Amsterdam (i.e.): Elsevier North-Holland.
Theuvsen, L., Janze, C., & Heyer, M. (2010). Agribusiness in Deutschland 2010. Unternehmen auf dem Weg in neue Märkte! Ernst &Young (Eds.). [Agribusiness in Germany 2010. Companies on the way to new markets!]. http://www.bv-agrar.de/bvagrar/agrarwelt/ausbildung/studie_agribusiness_2010.pdf. Accessed June 3, 2012.
Tohidi, H., & Jabbari, M. M. (2012). Different stages of innovation process. Procedia Technology, 1(1), 574–578.
Trott, P. (2002). Innovation management and new product development. Edinburgh Gate, Harlow: Financial Times Prentice Hall.
Wang, Y.-P., Chen, S.-H., Chang, K.-W., & Shen, Y. (2012). Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps. Precision Agriculture, 13(5), 553–567.
Wissenschaftsrat. (2006). Empfehlungen zur Entwicklung der Agrarwissenschaften in Deutschland im Kontext benachbarter Fächer (Gartenbau-, Forst- und Ernährungswissenschaften). [Recommendations for the development of agricultural sciences in Germany in the context of neighbouring scientific areas]. Koeln. http://www.wissenschaftsrat.de/download/archiv/agrarwissenschaften.pdf. Accessed June 1, 2012.
Yu, M., Segarra, E., Lascano, R., & Booker, J. (2003). Economic impacts of precision farming in irrigated cotton production. The Texas Journal of Agriculture and Natural Resource, 16(1), 1–14.
Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture, 36(2–3), 113–132.
This paper presents selected results from a comprehensive study regarding the German Agricultural innovation system. The study received funding from the Innovation Support Program of the German Federal Ministry of Food, Agriculture and Consumer Protection (BMELV) based on a German Parliament resolution (Grant 123-02.05-20.0076/10-H). This study included a Delphi survey, in which 150 experts from plant production, live stock farming and horticulture were contacted. The results from these three groups were quite comparable. The authors would like to thank Dr. Sven Lundie (Doeninghaus, Walker & Partner, moderator of the workshops), Judith Emmerling (student assistant at the Humboldt-University of Berlin, Department of Agricultural Economics) and all experts for participating in interviews, workshops and the survey.
About this article
Cite this article
Busse, M., Doernberg, A., Siebert, R. et al. Innovation mechanisms in German precision farming. Precision Agric 15, 403–426 (2014). https://doi.org/10.1007/s11119-013-9337-2
- Innovation processes
- Sectoral innovation system
- Expert interviews
- Delphi survey